MSc Mathieu Grapin

MD, PhD Charles Coutant

PharmD Jean-Marc Riedinger

Sylvain Ladoire

MD, PhD François Brunotte

MD, PhD Alexandre Cochet

Olivier Humbert

; Sylvain

; Olivier

Combination of breast imaging parameters obtained from 18F-FDG PET and CT scan can improve the prediction of breast-conserving surgery after neoadjuvant chemotherapy in luminal/HER2-negative breast cancer

Keywords: FDG, 18 Fluoro-deoxy-glucose BCS, Breast Conserving Surgery BV Total, Total Breast Volume ER, Estrogen Receptor HER2, Human Epidermal growth factor Receptor 2 HR, Hormone receptor MTV, Metabolic Tumor Volume NAC, Neoadjuvant Chemotherapy pCR, pathological Complete Response PR, Progesterone Receptor ROC, Receiver Operating Curve SBR, Scarff-Bloom-Richardson Triple Negative Breast Cancer US scan, UltraSound scan

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Neoadjuvant chemotherapy (NAC) is a safe and effective therapeutic approach for women with a large primary breast tumor for whom a mastectomy is initially recommended. It offers the advantage of down staging the disease before surgery, potentially reducing its extent. The main clinical benefit of NAC is an increase in the rate of breast-conserving surgery (BCS) [START_REF] Killelea | Neoadjuvant Chemotherapy for Breast Cancer Increases the Rate of Breast Conservation: Results from the National Cancer Database[END_REF][START_REF] Barranger | Effect of Neoadjuvant Chemotherapy on the Surgical Treatment of Patients With Locally Advanced Breast Cancer Requiring Initial Mastectomy[END_REF]. NAC does not change a patient's oncologic outcome compared with a mastectomy followed by adjuvant chemotherapy [3], but studies have demonstrated that a pathologic complete response (pCR) in both breast and nodes is a surrogate marker for better outcomes [START_REF] Mauri | Neoadjuvant Versus Adjuvant Systemic Treatment in Breast Cancer: A Meta-Analysis[END_REF][5][START_REF] Penault-Llorca | Comparison of the prognostic significance of Chevallier and Sataloff's pathologic classifications after neoadjuvant chemotherapy of operable breast cancer[END_REF]. Thus, pCR has become a crucial end-point in the neoadjuvant setting [5]. However, breast cancer is composed of different biological entities.

More than half of the women with breast cancer present the luminal/Human Epidermal Growth Factor Receptor 2 (HER2) negative subtype, defined by the expression of hormonal receptors (HR) but no over-expression of the human HER2, which corresponds to the luminal subtype [START_REF] Köninki | HER-2 positive breast cancer: decreasing proportion but stable incidence in Finnish population from 1982 to 2005[END_REF].

Compared with other subtypes, it has a more favorable outcome despite lower chemo-sensitivity [START_REF] Yoo | Impact of immunohistochemistry-based molecular subtype on chemosensitivity and survival in patients with breast cancer following neoadjuvant chemotherapy[END_REF]. Pathologic complete response is rarely achieved in this subtype, and its prognostic value is debatable [5,[START_REF] Colleoni | Chemotherapy is more effective in patients with breast cancer not expressing steroid hormone receptors: a study of preoperative treatment[END_REF][START_REF] Bhargava | Immunohistochemical surrogate markers of breast cancer molecular classes predicts response to neoadjuvant chemotherapy: A single institutional experience with 359 cases[END_REF][START_REF] Rouzier | Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy[END_REF][START_REF] Guiu | Pathological response and survival after neoadjuvant therapy for breast cancer: A 30-year study[END_REF][START_REF] Cortazar | Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis[END_REF]. Consequently, tumor shrinkage sufficient for BCS is a more reasonable aim than pCR in the luminal/HER2-negative subtype. Because the use of NAC for this subtype frequently challenged, there is a need for predictive biomarkers to optimize prescription. Tumor expression of Ki-67 is helpful, but not sufficient [START_REF] Lips | Breast cancer subtyping by immunohistochemistry and histological grade outperforms breast cancer intrinsic subtypes in predicting neoadjuvant chemotherapy response[END_REF]. The early metabolic response, evaluated with 18 Fluoro-deoxy-glucose ( 18 F-FDG) PET-CT, accurately predicts pCR in triple negative breast cancer (TNBC) and HER2-positive subtypes [START_REF] Avril | 18F-FDG PET/CT for Monitoring of Treatment Response in Breast Cancer[END_REF][START_REF] Berriolo-Riedinger | 18F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy[END_REF][START_REF] Humbert | HER2-positive breast cancer: 18F-FDG PET for early prediction of response to trastuzumab plus taxane-based neoadjuvant chemotherapy[END_REF]. However, studies have failed to demonstrate the value of 18 F-FDG PET to predict pCR in the luminal breast cancer subtype [START_REF] Humbert | Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using 18F-FDG PET in luminal HER2-negative breast cancer[END_REF][START_REF] Im | Usefulness of Combined Metabolic-Volumetric Indices of 18F-FDG PET/CT for the Early Prediction of Neoadjuvant Chemotherapy Outcomes in Breast Cancer[END_REF][START_REF] Groheux | Estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast tumors: early prediction of chemosensitivity with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography during neoadjuvant chemotherapy[END_REF]. The aim of the present study was to identify, for the luminal/HER2-negative breast cancer subtype, early metabolic and morphologic 18 FDG PET-CT imaging features predictive of BCS after NAC.

MATERIAL AND METHODS

Patients and study design

From November 2006 to July 2015, consecutive women referred to our institution for newly diagnosed stage IA to IIIA luminal/HER2-negative breast cancer (defined as hormonal receptorpositive, no HER2 over-expression) were retrospectively included in an on-going ancillary study of prospective current-care protocol in our institution. The inclusion criteria were: BCS was deemed not feasible on initial consultation (high tumor volume/breast volume ratio especially if it was a tumor of the inferior quadrants or retro-areolar) and decision to treat with NAC in order to decrease tumor size and potentially allow BCS after neoadjuvant treatment. For the surgeon there was not any objective clinical criterion of favorable tumor volume/breast volume ratio. The exclusion criteria were: NAC was not administrated with the intention of BCS (inflammatory or multifocal breast lesions), mastectomy was planned independently of tumor response to NAC (patient's desire, BRCA mutation), women under 18 years old or pregnant, women unwilling to undergo the two 18 F-FDG PET-CT exams, high blood glucose level (>9 mmol/l) before PET exams, or metastases on baseline PET. The medical team documented the non-opposition of the patient in source document and in the notice of information provided to the patient. This population overlaps those of previous articles published by our team [START_REF] Humbert | Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using 18F-FDG PET in luminal HER2-negative breast cancer[END_REF][START_REF] Humbert | Changes in 18F-FDG tumor metabolism after a first course of neoadjuvant chemotherapy in breast cancer: influence of tumor subtypes[END_REF].

Baseline clinical characteristics included age, menopausal status, tumor size and lymph node involvement evaluated on US scan. Lymph node involvement on US scan was confirmed by biopsy. Baseline histological characteristics, evaluated on the pre-treatment core needle biopsy, included histological type, tumor grading using the modified Scarff-Bloom-Richardson (SBR) system, architectural differentiation, nuclear polymorphism and number of mitosis. The molecular markers examined included Estrogen Receptor (ER) status, Progesterone Receptor (PR) status, and over-expression of HER 2.

Women received one of two possible treatment regimens, either a sequential intravenous chemotherapy with 5-Fluorouracile 500 mg/m 2 , Epirubicine 75 or 100 mg/m 2 and Cyclophosphamide 500 mg/m 2 (FEC 100, 3 courses: one course every 3 weeks) followed by taxanes (docetaxel 100 mg/m 2 for 3 courses: one course every 3 weeks or paclitaxel 80 mg/m 2 for 3 courses: one injection weekly for 9 weeks). 18 Fluoro-deoxy-glucose PET-CT exams were performed for baseline staging, and after the first course of NAC to evaluate tumor response.

Within one month after the last course of NAC, each patient was scheduled for an ultrasound scan (US), in some cases for a MRI and were examined by their surgeon to evaluate tumor response.

The surgeon then decided whether the patient should undergo BCS or a mastectomy. The following parameters rather directed the surgeon towards a BCS: low tumor volume/breast volume ratio, tumor of the external quadrants and concentric tumor response on the MRI. There was not any objective cut off for breast size or tumor size. If BCS was followed by a salvage mastectomy (whatever the reason, including incomplete microscopic resection), it was considered a mastectomy for our study. A pathologist examined the resected specimens to evaluate the surgical margin (clear/positive/close) and the pathological tumor response.

Histopathological analysis

Tumor samples were collected by needle core biopsy before the NAC. The specimens were fixed on buffered formalin, embedded in paraffin and cut in 4-µm thick sections with a microtome. IHC was performed with an indirect immunoperoxidase method using antibodies directed against HER2 oncoprotein, ER and PR (HER2: rabbit monoclonal prediluted antibody 4B5; ER: rabbit monoclonal prediluted antibody SP1; PR: rabbit monoclonal prediluted antibody 1E2, all Ventana Medical Systems, Tucson, AZ, USA;). ER and PR status were considered positive if tumor staining showed at least 10 % positive cells. HER2 status was graded according to the HercepTest scoring system.

Invasive tumors with scores of 3+ were considered positive. In case of 2+ scores, Fluorescence In Situ Hybridization (FISH) was performed, using the dual color HER2 and CEN17 probes (ZytoLight, SPEC HER2/CEN17 Dual ColorProbe Kit, ZytoVision GmbH, Bremerhaven, Germany). HER2 amplification was defined, according to ASCO/CAP criteria, by a ratio of HER2/CEN17 > 2. Tumor resection was considered as complete if there were clear margins of at least 2mm for ductal carcinoma in situ and no invasion of surgical section slices for invasive cancer. Pathological complete response was defined as no residual invasive cancer in the breast and nodes (ypT0/is ypN0) [5]. 18

F-FDG PET-CT exams

The first 18 F-FDG PET-CT was performed at baseline. Two different PET-CT imaging systems were used: a Gemini GXL PET-CT scanner from November 2006 to December 2010, and a Gemini TF PET-CT scanner from December 2010 to July 2015 (Philips Medical Systems, Eindhoven, The Netherlands). Patients were instructed to fast for at least 6 h before the intravenous injection of 5 MBq/kg of 18 F-FDG for Gemini GXL studies and 3 MBq/kg for Gemini TF studies. Emission and transmission scans from the brain to mid-thigh were acquired 60 min later. Scans restricted to the chest with patients in the prone position were started 90 min after the injection of 18 F-FDG. Emission data were corrected for dead time, random and scatter coincidences, and attenuation before reconstruction with the 3D-RAMLA (GEMINI GXL) or 3D OSEM (GEMINI TF) iterative algorithm methods. The second 18 F-FDG PET-CT was done just before the second course of NAC: a chest scan was completed 90 minutes after the injection of 18 F-FDG. For each patient, the same imaging system, 18 F-FDG activity, time from injection to acquisition, and reconstruction parameters were used for baseline and post-treatment studies to obtain good intra-subject standardization. The image voxel counts were calibrated to activity concentration (Bq/ml) and decay corrected using the time of tracer injection as the reference.

On the chest-restricted baseline PET acquisiton, the whole homolateral mammary gland was delineated on transaxial consecutive CT slices to calculate total Breast Volume (BVtotal) (cm3). The primary breast tumor was manually delineated on the baseline and interim PET studies, using a visual method for tumor metabolic delineation on PET images. The metabolic tumor contour obtained was then checked on the CT slices and adjusted to the morphologic edge of the breast lesion if needed.

Two operators worked complementarily to record these data (a senior nuclear doctor and a resident), without overlapping.

The maximum Standardized Uptake Value (SUV max ), the mean SUV (SUV mean ), Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG = SUV mean x MTV) of the primary tumor were calculated at baseline and interim exams and measured on chest prone scan. Baseline MTV 1 /BV total ratio and MTV 2 /BV total ratio were reported. Measured SUV max were systematically corrected for Body Surface Area (BSA) and glycaemia, as detailed in our previous studies [START_REF] Berriolo-Riedinger | 18F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy[END_REF][START_REF] Humbert | Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using 18F-FDG PET in luminal HER2-negative breast cancer[END_REF]. The metabolic response between baseline and interim PET were calculated using theses formulas:

ΔSUV max (%)=100 x (SUV max2 -SUV max1 )/SUV max1 ΔSUV mean (%)=100 x (SUV mean2 -SUV mean1 )/SUV mean1 ΔMTV(%)=100 x (MTV 2 -MTV 1 )/MTV 1 ΔTLG(%)=100 x (TLG 2 -TLG 1 )/TLG 1

Statistical analysis

Statistical analysis was performed with the use of WinSTAT software (Microsoft, Redmond, Washington, USA) and Systat software (Systat Inc, Evanston, IL). Data were described as frequency (percentage) or mean (± Standard Deviation (SD)) and median (range). Correlations between 18 F-FDG PET-CT data and surgery were assessed with the Mann-Whitney test, and associations between qualitative variables and final surgery procedures were evaluated using the chi-square test and fisher test. The optimal cutoff values for continuous variables correlated with the final surgery procedure were determined using receiver operating curve (ROC) analyses. Univariate and multivariate analysis logistic regression analyzes were used to identify predictive factors of BCS. All p-values were twosided and considered significant when below 0.05.

RESULTS

Patient characteristics

Seventy-seven women were included. The patients' clinical and biological characteristics at baseline are shown in Table 1. Median age was 52 years [26 -75]. Median tumor size, which was assessed with breast US scan and/or mammogram, was 3.2 cm [1.5 -7.5]. US scan results showed that 56% (43/77) of women had lymph node involvement. Sixty-two percent (47/77) of tumors were SBR II, 87% (67/77) were invasive ductal carcinoma and the remaining tumors were lobular carcinoma. The median breast volume (BV total ) on 18 F-FDG PET-CT was 540 cm 3 [149 -2150].

The median SUV max1 was 5.1 [1.7 -23.9], median MTV 1 was 6.7 cm 3 [0,9 -57.8], and median BCS was performed in 64% (49/77) of the women. Mastectomy was performed in the remaining 36% (28/77) including 8 salvage mastectomies due to incomplete microscopic resection on BCS.

Relationship between clinico-histopathological and imaging parameters with final surgery

There was a significant difference of PR status between the BCS and mastectomy subgroups: 75% of patients (21/28) overexpressed PR in the mastectomy group whereas 92% of patients (45/49) overexpressed PR in BCS group (p = 0.04) (Table 2). There was no significant difference in patient age, tumor size, tumor location, lymph node status, SBR grade, or ER expression, protocol of chemotherapy. The patient with pCR who was submitted to mastectomy had a tumor residue on US scan by the end of NAC (false positive).

Analysis of imaging features revealed that patients with a mastectomy had lower BV total (p = 0.002), lower decrease ΔMTV (p = 0.03) and were prone to lower SUV max2 (p = 0.05) (Table 3). Baseline MTV, TLG, MTV 1 /BV Total ratio and MTV2/BV Total ratio were not significantly associated with the final surgery. In multivariate analysis, the following parameters were associated with a BCS: ΔMTV OR = 0. (p = 0.06). Using ROC curve analysis, the optimal threshold of the different continuous and predictive PET-CT variables were 496 cm 3 for BV Total (AUC = 0.71), -17% for ∆MTV and 3.6 for SUV max2 (AUC = 0.65 and 0.64, respectively) (Table 4). Focusing on two main predictive factors of BCS (BV total and ΔMTV), we identified three subgroups of women with significantly different odds of undergoing BCS (p < 0.001) (Table 5): High probability of BCS was defined as women with BV total (> 496 cm 3 ) and good tumor shrinkage ( ΔMTV < -17%): The probability of BCS in this group was 82% (28/32 patients) (Figure 1a & 1b).

Intermediate probability of BCS was defined as women with high BV total (> 496 cm 3 ) and poor tumor shrinkage (∆MTV ≥ -17%) or women with low BV total (≤ 496 cm 3 ) and good tumor shrinkage (∆MTV < -17%). The probability of BCS in this group was 62% (16/26 patients).

Low probability of BCS was defined as women with low BV total (≤ 496 cm 3 ) and poor tumor shrinkage (∆MTV ≥ -17%). The probability of BCS in this group was 29% (5/17 patients) (Figure 2a &2b).

DISCUSSION

Being able to predict at baseline which women with luminal/HER2 negative tumors will have sufficient tumor response for breast preservation is an important clinical issue. The clinical and biological markers in this subtype are not sufficient to accurately determine the indication of NAC at an individual level. The surgeon can weigh clinical parameters such as breast size, tumor location or tumor baseline volume, but these criteria are based on subjective evaluation. Using clinico-pathologic data, Rouzier et al. has prospectively developed nomograms that can be used to predict the probability of residual tumor size and eligibility for breast conservation therapy after NAC [START_REF] Rouzier | Development and validation of nomograms for predicting residual tumor size and the probability of successful conservative surgery with neoadjuvant chemotherapy for breast cancer[END_REF], but the conception of this nomogram did not take the tumor immuno-histochemical subtype into account and was thus not designed for the specific Luminal/HER2 subtype. This nomogram did not include any imaging data.

Because glucose metabolism is increased in breast cancer, the monitoring of metabolic response with 18 F-FDG PET has been proposed for the early prediction of pCR [START_REF] Humbert | Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using 18F-FDG PET in luminal HER2-negative breast cancer[END_REF][START_REF] Groheux | 18 F-FDG PET/CT in the early prediction of pathological response in aggressive subtypes of breast cancer: review of the literature and recommendations for use in clinical trials[END_REF]. The accuracy of PET is high in TNBC and HER2 positive subtypes [START_REF] Avril | 18F-FDG PET/CT for Monitoring of Treatment Response in Breast Cancer[END_REF][START_REF] Berriolo-Riedinger | 18F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy[END_REF][START_REF] Humbert | HER2-positive breast cancer: 18F-FDG PET for early prediction of response to trastuzumab plus taxane-based neoadjuvant chemotherapy[END_REF]. In the Luminal/HER2 negative subtype, previous studies failed to demonstrate its predictive ability. Some authors demonstrated that a near-pCR could be predicted [START_REF] Koolen | Sequential (18)F-FDG PET/CT for early prediction of complete pathological response in breast and axilla during neoadjuvant chemotherapy[END_REF], but the prognostic value of near-pCR failed to be demonstrated and is not considered a good clinical end-point [5] In the present study, we recorded and quantified the morphologic and metabolic PET-CT imaging characteristics before and after the first course of NAC. Most of the patients (86% in mastectomy group and 94% in BCS group) received 3 cycles of FEC followed by 3 cycles of taxanes. Thus, the response to taxanes is not evaluated by the design of our study. It has been shown in the adjuvant setting than replacing the last 2 FEC 100 cycles of 6 FEC100 regimen by 4 Taxol does not lead to a discernable DFS or OS advantage [START_REF] Delbaldo | Association Européenne de Recherche en Oncologie (AERO), A phase III adjuvant randomised trial of 6 cycles of 5fluorouracil-epirubicine-cyclophosphamide (FEC100) versus 4 FEC 100 followed by 4 Taxol (FEC-T) in node positive breast cancer patients (Trial B2000)[END_REF]. However, in the neoadjuvant setting, anthracycline and taxane-based chemotherapy would give a higher rate of pCR over the anthracycline-based chemotherapy [START_REF] Dodiya | Neoadjuvant chemotherapy in patients with locally advanced breast cancer: A pilot-observational study[END_REF]. In our study, almost the same proportion of patients received 3 FEC 100 and 3 taxanes in each group, but the midcourse switch to taxanes is a limit. Contrary to previous studies, we did not try to predict pCR nor near-pCR, but rather aimed to identify Luminal/HER2 negative patients who were most likely to qualify for breast conservation therapy, which is the main clinical advantage of NAC in this tumor subtype.

Two main predictive imaging parameters of BCS were found: Breast morphology: women with a BV Total over 496 mL, quantified precisely on the baseline CT compound of the PET system, were more likely to have BCS.

Tumor chemosensitivity: positive metabolic response after the 1 st cycle of treatment, defined by ΔMTV < -17%, was associated with a higher probability of BCS.

We combined these two characteristics to identify three groups of patients with varying odds of undergoing BCS. It is interesting to notice that BCS gradually increases from 29% in the most unfavorable group (low BV Total and poor tumor shrinkage) to 82% in the most favourable one (high BV Total and good tumor shrinkage). Surprisingly, we observed that SUV max2 was higher in BCS group (4.3) compared to mastectomy group (3.0). But SUV max2 can hardly be compared between these two groups because SUV max1 was also higher in the BCS group (5.8 versus 4.3 respectively). In the literature, ΔMTV was also found to be a significant prognostic factor for pathological response in a population including several breast tumor subtypes [START_REF] Groheux | Estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast tumors: early prediction of chemosensitivity with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography during neoadjuvant chemotherapy[END_REF][START_REF] Choi | The value of pre-and post-neoadjuvant chemotherapy F-18 FDG PET/CT scans in breast cancer: comparison with MRI[END_REF]. Groheux et al. also demonstrated a strong correlation between ∆MTV and good pathologic response (-21% ± 31% in non-responders vs -57% ± 37% in responders; P = 0.0002), superior to the correlation with ∆SUV max but lower than those of ∆TLG [START_REF] Groheux | Estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast tumors: early prediction of chemosensitivity with (18)F-fluorodeoxyglucose positron emission tomography/computed tomography during neoadjuvant chemotherapy[END_REF]. Among the biologic parameters, negative PR status (p = 0.04) and the luminal B subtype (p = 0.02) were predictive of a pathologic response.

Seung Hyun Son et al. also showed that ΔMTV was correlated with disease free survival [START_REF] Hyun | Volume-based metabolic tumor response to neoadjuvant chemotherapy improves outcome prediction in breast cancer[END_REF].

When surgery is performed as first-line treatment, BV Total should be measured in breast cancer because the tumor/BV Total ratio is of significance when evaluating whether BCS is appropriate [START_REF] Cochrane | Cosmesis and satisfaction after breast-conserving surgery correlates with the percentage of breast volume excised[END_REF][START_REF] Bulstrode | Prediction of cosmetic outcome following conservative breast surgery using breast volume measurements[END_REF].

Nevertheless, we are the first study to demonstrate the clinical interest of BV Total measurement at baseline to predict final BCS, which remains the main goal of NAC in this setting. The CT component of PET, used at baseline for tumor staging, can accurately measure BV Total . The BV Total threshold to predict BCS was approximately 500cm 3 in our study. It is worth noting that we did not find any predictive value for baseline tumor volume or Tumor/BV Total ratio. Lastly, the manual delineation of tumors on the PET-CT images may be a limitation. Manual segmentation is time consuming, labor intensive and operator-dependent, and the intra-and inter-operator variability of the resulting delineations make this method less precise and reproducible than a fixed threshold [START_REF] Paidpally | Interreader agreement and variability of FDG PET volumetric parameters in human solid tumors[END_REF][START_REF] Hatt | Defining radiotherapy target volumes using 18F-fluoro-deoxyglucose positron emission tomography/computed tomography: still a Pandora's box?: in regard to Devic et al[END_REF][START_REF] Nestle | Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer[END_REF][START_REF] Foster | A review on segmentation of positron emission tomography images[END_REF].

Nevertheless, in breast cancer, differentiation of the tumor uptake from the surrounding uptake of the mammary gland is an image segmentation issue that cannot be rigorously addressed using fixed and adaptive threshold-based methodologies. The fixed threshold that we tried for this study (either fixed SUV or specific percentage of SUV max ) failed to delineate low FDG-avid and heterogeneous breast tumors; they led to inconsistent tumor volumes. Despite a high inter-reader agreement [START_REF] Paidpally | Interreader agreement and variability of FDG PET volumetric parameters in human solid tumors[END_REF], the literature has demonstrated that fixed thresholds cannot reliably define MTV because of their deterministic and binary nature, as long as tumor uptake is variable, spatially heterogeneous, and dependent on a large number of data acquisition and image reconstruction parameters [START_REF] Hatt | Defining radiotherapy target volumes using 18F-fluoro-deoxyglucose positron emission tomography/computed tomography: still a Pandora's box?: in regard to Devic et al[END_REF][START_REF] Nestle | Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer[END_REF][START_REF] Foster | A review on segmentation of positron emission tomography images[END_REF].

Adaptive thresholds are likely to provide sufficient accuracy in simple cases, but require precise tuning for a specific scanner, the reconstruction type, and even the size of the patient [START_REF] Hatt | Defining radiotherapy target volumes using 18F-fluoro-deoxyglucose positron emission tomography/computed tomography: still a Pandora's box?: in regard to Devic et al[END_REF][START_REF] Foster | A review on segmentation of positron emission tomography images[END_REF].

Moreover, fixed thresholds generally make too many simplifying assumptions to be considered for complex situations such as low-contrast lesion with complex shape and heterogeneous uptake [START_REF] Hatt | Defining radiotherapy target volumes using 18F-fluoro-deoxyglucose positron emission tomography/computed tomography: still a Pandora's box?: in regard to Devic et al[END_REF][START_REF] Foster | A review on segmentation of positron emission tomography images[END_REF].

Consequently, in the absence of more appropriate segmentation tools such as iterative, stochastic and learning-based thresholding methods, it is assumed that fixed thresholds are to be avoided and manual tumor delineation should be favored [START_REF] Hatt | Defining radiotherapy target volumes using 18F-fluoro-deoxyglucose positron emission tomography/computed tomography: still a Pandora's box?: in regard to Devic et al[END_REF][START_REF] Wu | Autocontouring and manual contouring: which is the better method for target delineation using 18F-FDG PET/CT in non-small cell lung cancer?[END_REF].

There are several possibilities for further study. Firstly, it would be of great interest to develop nomograms including relevant biological biomarkers, such as Ki-67 and imaging parameters to improve accuracy in predicting breast conservation after NAC. Secondly, MRI was not systematically performed and we could therefore not compare PET and MRI for early tumor volume changes. An MRI can provide key data such has the extent of the residual tumor after NAC [START_REF] García-Lallana | Using magnetic resonance imaging for staging can change the therapeutic management in patients with breast cancer[END_REF] and is a valuable tool for the surgeon in decision-making following first line treatment.

When compared with a mammography and an ultrasound after NAC, the MRI better predicts pathological response [START_REF] Marinovich | Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis[END_REF]. A pre-operative MRI can reduce the rate of tumor-positive resection margins substantially (from 29% to 16%) [START_REF] Obdeijn | Preoperative breast MRI can reduce the rate of tumor-positive resection margins and reoperations in patients undergoing breast-conserving surgery[END_REF]. Currently, no study has demonstrated that an MRI can accurately predict the final conservative surgery in luminal/HER2 negative breast cancers at baseline. 18 F-FDG PET and MRI could have complementary functions with respectively a high sensibility and a high specificity to predict response to NAC [START_REF] Li | MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis[END_REF][START_REF] Kitajima | Assessment of tumor response to neoadjuvant chemotherapy in patients with breast cancer using MRI and FDG-PET/CT-RECIST 1.1 vs. PERCIST 1.0[END_REF]. If we look further, 18 F-FDG PET/MRI is a useful method for estimating both morphologic and metabolic tumor volume [START_REF] Romeo | Simultaneous PET/MRI assessment of response to cytotoxic and hormone neo-adjuvant chemotherapy in breast cancer: a preliminary report[END_REF].

Even to estimate breast volume, MRI is highly accurate [START_REF] Choppin | The accuracy of breast volume measurement methods: A systematic review[END_REF].

In the present study we did not directly correlate MTV with morphologic volume. But previous studies have underlined the discrepancies between these volumes [START_REF] Paulino | Comparison of CT-and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-andneck cancer[END_REF]. Indeed, the MTV measured at baseline and after one cycle, only includes the part of the tumor which is "FDG-positive" and, by nature, excludes the part of the tumor morphologic volume with no high glycolytic activity, such as necrosis, fibrosis or scar. The part of the tumor volume is not negligible after the induction of chemotherapy. The MTV represents the dual characteristics of tumor extent, a morphologic characteristic, and the intensity of FDG uptake by tumor tissues, a biological tumor characteristic.

Thus, the MTV measured on PET imaging, carry a unique biological information not obtained with conventional morphological imaging such as US scan. For example, 18 F-FDG-PET imaging can differentiate the viable portion of a heterogeneous tumor from fibrosis or necrotic portions. In future studies, it would be of high interest to assess if early tumor volume changes, measured on functional MRI, could also predict surgical outcome in the neoadjuvant luminal breast cancer setting. A precocious determination of the success percentage of BCS after one course of NAC could help to adapt therapeutics; by adding hormonotherapy to chemotherapy [START_REF] Generali | Benefit of the addition of hormone therapy to neoadjuvant anthracycline[END_REF], changing drug regimen [45], or leading to mastectomy without waiting for the end of the 6 courses of NAC.

CONCLUSIONS

For patients with Luminal/ HER2 negative breast cancer, the imaging features of both the tumor and the mammary gland, obtained with a 18 F-FDG PET-CT at baseline and after the first cycle of NAC, may enable the physician to evaluate the probability of final conserving surgery, if confirmed by prospective studies. Imaging data resulting from these techniques could thus help clinicians and breast cancer patients to optimize clinical decision-making. It is likely that the integration of imaging parameters such as BV Total and ∆MTV in future will improve the accuracy of nomograms in this setting.

Legends for figures

Figure 1a

. and 1b. 18 Fluoro-deoxy-glucose PET-CT images of a 26 year-old woman with high probability of Breast Conserving Surgery (BCS) group: Total Breast Volume (BV total ) = 1051 cm 3 (>496 cm 3 ) and ∆Metabolic Tumor Volume (∆MTV) = -79.7% (<-17%). After Neoadjuvant Chemotherapy (NAC), she underwent conservation surgery. The MTV was delineated in orange while the BV total was delineated in blue. Figure 1a illustrates 18 18 Fluoro-deoxy-glucose PET-CT images of a 38 year-old woman with a low probability of BCS group: BV total = 278 cm 3 (≤ 496 cm 3 ) and ∆MTV = 0 % (≥ -17%). After NAC, she had a mastectomy. The MTV was delineated in orange while the BV total was delineated in blue. Figure 2a illustrates 18 All the other 18 Fluoro-deoxy-glucose PET-CT parameters evaluated were not significantly predictive of pCR with ROC Curve analyses.

Legends for tables:

  TLG 1 was 21.3[1.7 -486.7]. In terms of tumor metabolic response, median ΔSUV max was -24% [-89 -+101], median ΔTLG was -43% [-97 -+37] and median ΔMTV was -30% [-87 -+18].

  27 [95% CI, 0.08 -0.87] (p = 0.03), SUV2 max OR = 3.96 [95% CI, 1.27 -12.36] (p = 0.02). These two parameters were at the limit of significance: BV total OR = 3.11 [95% CI, 1.00 -9.63] (p = 0.05), positive PR OR = 4.53 [95% CI, 0.29 -22.32]

  F-FDG PET-CT before the first course of NAC (the two images on the left show an axial view, the two images on the right show a sagittal view, the two images on the top show combined PET CT-scan, the two images on the bottom show CT-scan only); figure 1b illustrates 18 F-FDG PET-CT after the first course of NAC.

Figure 2a and 2b.

  Figure 2a and 2b.18 Fluoro-deoxy-glucose PET-CT images of a 38 year-old woman with a low

  F-FDG PET-CT before the first course of NAC NAC (the two images on the left show an axial view, the two images on the right show a sagittal view, the two images on the top show combined PET-CT scan, the two images on the bottom show CT-scan only); figure 2b illustrates18 F-FDG PET-CT after the first course of NAC.

  based chemotherapy for breast cancer: comparison of predicted and observed pCR, J. Cancer Res. Clin. Oncol. 144 (2018) 601-606. doi:10.1007/s00432-017-2574-4. [45] T. Zhu, C.L. Liu, Y.F. Zhang, Y.H. Liu, F.P. Xu, J. Zu, G.C. Zhang, X.R. Li, N. Liao, K. Wang, A phase II trial of dose-dense (biweekly) paclitaxel plus carboplatin as neoadjuvant chemotherapy for operable breast cancer, Breast Cancer Res. Treat. 156 (2016) 117-124. doi:10.1007/s10549-016-3735-x.

Table 1 . Patient characteristics.

 1 Total breast volume (BV Total ), Human epidermal growth factor receptor 2 (HER 2), Standard Uptake Value (SUV), Metabolic Tumor Volume (MTV), Total Lesion Glycolysis (TLG)

Table 2 . Clinic-pathological-biological tumor characteristics according to the surgery performed.

 2

	Scarff-Bloom-Richardson (SBR), Human Epidermal growth factor Receptor 2 (HER 2), 5-
	Fluorouracile, Epirubicine and Cyclophosphamide (FEC), Pathological Complete Response
	(pCR), Fluorescent In Situ Hybridation (FISH).
	NS = not significant (p>0.05). *The Chi-squared test was performed.

Table 3 . PET data comparison between mastectomy and breast conserving surgery groups.

 3

	Total breast volume (BV Total ), Metabolic Tumor Volume (MTV), Standard Uptake Value (SUV), Total
	Lesion Glycolysis (TLG).
	NS = not significant (p>0.05). *Mann-Whitney test was performed.

Table 4 . ROC Curve data.

 4 Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), Standard Deviation (SD), Standard Uptake Value (SUV), Metabolic Tumor Volume (MTV), pathological Complete response (pCR).

Table 5 . Groups according to BV Total and tumor shrinkage (∆MTV).

 5 Total breast Volume (BV Total ), Metabolic Tumor Volume (MTV). The Chi-squared test was performed.

	Patient characteristic	Number
	Total patients	77
	Age (years) Median [range]	52 [26 -75]
	Anatomopathological characteristics	Number (%)
	Type of cancer
		67 (87)
	Invasive ductal carcinoma
		10 (13)
	Lobular carcinoma
	Tumor staging
	cT1	8 (10)
	cT2	61 (79)
	cT3	6 (8)
	cT4	0
	Missing values	2 (3)
	Estrogen receptor status
	Positive	74 (96)
	Negative	3 (4)
	Progesterone receptor status
	Positive	66 (86)
	Negative	11 (14)
	HER 2 status
	Overexpressed	0 (0)
	Not overexpressed	77 (100)
	Baseline morphologic or metabolic imaging	Median [range]
	characteristics
	Tumor size on US scan or mammography (cm)	3.2 [1.5 -7.5]
	BV Total (cm 3 )	540 [149 -2150]

*

Table 1 .

 1 Patient characteristics.

				Mastectomy (%)	Breast	P*
					conserving
					surgery (%)
	N			28 (100)	49 (100)
	Age (years)				0.11*
	≤ 50			15 (54)	17 (35)
	> 50			13 (46)	32 (65)
	Tumor staging			0.11*
	cT1			1 (4)	7 (15)
	cT2			22 (81)	39 (81)
	cT3			4 (15)	2 (4)
	2 patients with missing values
	were	not	included	in
	calculations
	Tumor location			0.88*
	External quadrants		21 (75)	39 (80)
	Internal quadrants		15 (54)	23 (47)
	Retro-areolar			6 (21)	9 (18)
	Clinical lymph node staging		0.11*
	cN0			9 (32)	25 (51)
	cN+			19 (68)	24 (49)
	Tumor grading			0.62*
	SBR I			4 (14)	6 (12)
	SBR II			18 (64)	29 (59)
	SBR III			5 (18)	14 (29)
	Estrogen receptor status		0.30**
	Positive			28 (100)	46 (94)

Total breast volume (BV Total ), Human epidermal growth factor receptor 2 (HER 2), Standard Uptake Value (SUV), Metabolic Tumor Volume (MTV), Total Lesion Glycolysis (TLG)

grant from funding agencies in the public, commercial, or not-for-profit sectors. Declarations of interest: none.


Valérian Cece
email: valerian.cece@orange.fr.

Emma Guillet-Descas

Virginie Nicaise

Noémie Lienhart

Guillaume Martinent

Longitudinal trajectories of emotions among young athletes involving in intense training centres: Do emotional intelligence and emotional regulation matter?

Keywords: Emotional intelligence, Emotional regulation, Latent class growth analysis, Sport emotions, Young athletes

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Longitudinal Trajectories of Emotions among Young Athletes Involving in Intense Training

Centres: Do Emotional Intelligence and Emotional Regulation Matter?

There are a growing interest and an increase in the amount of research focusing on emotions in sport psychology [START_REF] Campo | Emotions in team contact sports: A systematic review[END_REF][START_REF] Cerin | Temporal patterning of competitive emotions: A critical review[END_REF][START_REF] Hanin | Emotions in sport: Current issues and perspectives[END_REF]. Emotions are central in a sport setting because of their significant influence on performance and well-being [START_REF] Hanin | Emotions in sport: Current issues and perspectives[END_REF][START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF][START_REF] Mccarthy | Positive emotion in sport performance: current status and future directions[END_REF]. For instance, pleasant emotions (e.g. vitality, satisfaction) have been associated to positive outcomes such as mental health, performance or engagement [START_REF] Lyubomirsky | The benefits of frequent positive affect: Does happiness lead to success[END_REF][START_REF] Riemer | Development of the athlete satisfaction questionnaire (ASQ)[END_REF] whereas unpleasant emotions such as anxiety, anger, and depression have been associated to negative consequences (e.g. athlete burnout) and/or positive consequences depending on the situation and/or individual [START_REF] Beedie | The profile of mood states and athletic performance: Two meta-analyses[END_REF][START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF].

The Cognitive-Motivational-Relational theory (CMRT; [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF] postulates that the experience of discrete emotions depends on the singular way an athlete appraises the situation in which he is involved. In this perspective, emotion refers to an organized psychophysiological reaction (subjective experience, facial expression, physiological changes) to ongoing relationships with the environment reflecting the transaction between a person and his environment [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF]. Previous studies have suggested that the environment in which athletes are involved influences their emotional experiences (Campo et al., 2012). Hence, intensive training centres could influence the emotions experienced by young elite athletes because of the daily biological, social and psychological demands they have to cope with and/or the win-at-all-cost atmosphere that can reign in these centres [START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF]. In contrast, other authors have highlighted the facilitative role played by the sport competition context in the development of a wide variety of transferable skills for young elite athletes [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF]. Consequently, being involved in an intensive training centre is not an identical experience for all young elite athletes and different athletes could experience distinct emotional trajectories across the competitive season [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF][START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF][START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF]. Moreover, previous studies have provided evidence of the variability of emotional experiences across the competitive season as the daily constrains and competitive calendar can modulate athletes' emotional dynamics during the season [START_REF] Cerin | Temporal patterning of competitive emotions: A critical review[END_REF][START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF][START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF].

Although athletes experience a wide variety of pleasant and unpleasant discrete emotions in a sport setting [START_REF] Hanin | Emotions in sport: Current issues and perspectives[END_REF]Martinent, Campo, & Ferrand, 2012), previous studies have historically focused on negative discrete emotions, especially on anxiety [START_REF] Cerin | Temporal patterning of competitive emotions: A critical review[END_REF]. However, in line with the development of positive psychology [START_REF] Lyubomirsky | The benefits of frequent positive affect: Does happiness lead to success[END_REF], recent researches have provided evidence for considering both positive and negative emotions because positive emotions do not simply refer to the absence of negative ones and vice versa [START_REF] Folkman | Stress: appraisal and coping[END_REF][START_REF] Lundqvist | Positive emotions are not simply the absence of negative ones: Development and validation of the Emotional Recovery Questionnaire (EmRecQ)[END_REF][START_REF] Mccarthy | Positive emotion in sport performance: current status and future directions[END_REF]. Nevertheless, if some discrete emotions received considerable attention such as anxiety, anger, or happiness [START_REF] Cerin | Predictors of competitive anxiety direction in male Tae Kwon Do practitioners: A multilevel mixed idiographic/nomothetic interactional approach[END_REF][START_REF] Cerin | Temporal patterning of competitive emotions: A critical review[END_REF][START_REF] Mccarthy | Positive emotion in sport performance: current status and future directions[END_REF], other are currently poorly investigated in the sport context (e.g., love, harmony, vitality; [START_REF] Lundqvist | Positive emotions are not simply the absence of negative ones: Development and validation of the Emotional Recovery Questionnaire (EmRecQ)[END_REF]. Indeed, each discrete emotion could provide specific information [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF] and complete knowledge about the emotional experience of individuals.

To take into account the dynamic and the heterogeneity of naturally-occurring emotional trajectories emerging across the competitive season, researchers have to use a methodological approach specifically designed to reveal subgroups of athletes belonging to particular emotional trajectories [START_REF] Laursen | Person-centered and variable-centered approaches to longitudinal data[END_REF]. Longitudinal person-centred methods such as Latent Class Growth Analyses (LCGA) are suitable to identify several subgroups of athletes characterised by distinct emotional temporal patterns (or emotional trajectories) (Rindfleisch, Malter, Ganesan, & Moorman, 2008). LCGA trajectories are characterized by an intercept, a linear slope and a quadratic slope. The intercept refers to the value at which a line cuts a coordinate axis which could be considered as low, moderate or high. The slopes define the dynamics of a selected-variable across the measurement times.

The linear slope could be characterized as increasing (i.e. growing shape across the time), decreasing (i.e. declining shape across the time) or stable (i.e. no change across time) whereas the quadratic slope could be defined as positively unstable (i.e. U-shaped), negatively unstable (i.e. inverted U-shaped) or stable (i.e. no change across time). The examination of emotional trajectories across the competitive season could provide knowledge about the various emotional responses to the daily constraints of competitive atmosphere across the season. This approach could allow identifying some functional (i.e. theoretically associated to positive outcomes) or dysfunctional (i.e. theoretically associated to negative outcomes) subgroups into a global population. Thus, the emotional trajectories could be useful for both researchers and practitioners in providing further insights regarding the longitudinal everyday emotional

experience. Yet only one study has already used such a methodology in sport psychology to examine longitudinal trajectories of positive and negative affective states with a sample of 265 adolescent elite hockey players followed across 3 measurement points during the first 11 weeks of a season [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF]. LCGA results have provided evidence for 3 trajectories of positive affect (medium and decreasing, high and decreasing, unstable) and 3 trajectories of negative affect (low and unstable, medium and unstable, high and decreasing). Furthermore, athletes' memberships of trajectories of positive and negative affective states have been predicted by theoretically driven predictors assessed at the start of the season such as self-determination, need satisfaction, athletic identity, and school identity. These preliminary results have suggested that multinomial heterogeneity in longitudinal affective states needs to be accounted for in future research examining a wider range of unpleasant and pleasant discrete emotions [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF]. Moreover, emotional trajectories of affective states have been based on a selected part of the competitive season (11 weeks) rather than on the whole competitive season.

In addition to identify distinct naturally-occurring emotional trajectories of a wide variety of unpleasant and pleasant emotions experienced by young athletes in intensive training centres, it could be particularly useful to explore the determinants of those emotional trajectories. In the present study, we focused on two individual-difference variables likely to predict membership of athletes' emotional trajectories across the competitive season:

Emotional regulation (ER) and emotional intelligence (EI). ER refers to the process through which individuals influence which emotions they experience, when and how they experience and express them [START_REF] Gross | The emerging field of emotion regulation: an integrative review[END_REF]. Previous sport studies have shown that: (a) ER is linked to sport performance as well as to athletes' emotional experience [START_REF] Jones | Controlling emotions in sport[END_REF][START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF]Wagstaff, 2014); and (b) athletes use a wide variety of ER strategies to manage the emotions experienced in competition [START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF] and during training [START_REF] Nicholls | Stressors, coping, and coping effectiveness among professional rugby union players[END_REF]. Extending CMRT [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF], the process model of emotion regulation (PMER) postulates that some ER strategies have an impact at different stages of the emotion generation process [START_REF] Gross | Emotion regulation: Conceptual foundations[END_REF]. PMER also helps to structure the variety of ER strategies distinguishing two ER families: Antecedent-focused and response-focused ER strategies. Antecedent-focused strategies act before the emotion response tendencies become fully activated (early in the emotion-generative process). One of the most investigated antecedent-focused ER strategies is reappraisal, which involves the positive reinterpretation of threatening situations or highlighting the positive consequences of emotion-eliciting situations [START_REF] Gross | Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being[END_REF][START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF]. In line with CMRT, a positive reappraisal is supposed to positively change cognitions about the situation and may lead to high level of positive emotions and low level of negative ones [START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF].

In contrast, response-focused strategies are aimed at modifying emotional response tendencies after they have already been triggered [START_REF] Gross | Emotion regulation: Conceptual foundations[END_REF]. One of the most investigated response-focused ER strategies is emotional suppression, which is designed to influence behavioral emotion responses by suppressing the expressive emotion response [START_REF] Christophe | Évaluation de deux stratégies de régulation émotionnelle: la suppression expressive et la réévaluation cognitive[END_REF]. Previous studies conducted in general (Gross & Thomson, 2007) and sport psychology [START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF]Wagstaff, 2014) have provided strong evidence that emotional suppression is accompanied by a heavy cognitive, physiological, and social cost compared with reappraisal. Thus, the frequent and steady repetition of this costly modulation of emotional responses could disturb the positive emotions or foster the experience of negative emotions [START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF].

EI is another individual-difference variable examined in the present study which is linked to emotional experience [START_REF] Mayer | Personal intelligence expressed: A theoretical analysis[END_REF]. EI refers to the abilities to identify, understand, regulate, and use one's and others' emotions [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF][START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF]Schutte, Malouff, Simunek, McKenley, & Hollander, 2002). A hierarchical structure of the concept of EI has been proposed, leading some researchers to use a global score of EI [START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF]. In particular, a bulk of studies have provided evidence that a global score of EI could be used by researchers and practitioners as they simultaneously encompass all the aforementioned dimensions of EI [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF][START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF]. Based on the tenet of the CMRT [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF], the level of global EI could impact the appraisal of a competitive situation. This modification of cognitive evaluation lead to positive or negative discrete emotions [START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF].

More precisely, athletes characterised by low scores of EI experienced high levels of anger and confusion whereas athletes characterised by high scores of EI reported low levels of anger, confusion, depression, fatigue and tension and high levels of calm and joy [START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF]). In the sport psychology literature, EI has been positively linked to athletes' pleasant emotion and sport performance and negatively linked to unpleasant emotions in many sports [START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF]. However, most of the previous studies have used a cross-sectional design, preventing researcher to clearly disentangle the temporal ordering between EI and emotions. Yet, in line with CMRT [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF], we hypothesise that EI could predict the incoming positive and negative emotions.

In sum, emotional experiences are a crucial aspect of competitive sport especially for young elite athletes in intensive training centres as they are confronted with daily constraints.

Considering that the emotional process is fluctuant across time [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF] and that all young elite athletes do not necessarily experience the same emotions when involved in a demanding environment [START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF], a LCGA approach (longitudinal personcentred method) seems suitable to examine longitudinal emotional trajectories of a wide variety of pleasant and unpleasant discrete emotions. Thus, in the present study, we explored whether several subgroups of athletes representing distinct emotional trajectories emerged from LCGAs for a wide variety of sport emotions (anger, anxiety, sadness, confidence, happiness, harmony, love and vitality). Furthermore, we examined whether athletes' dispositional ER and EI scores at the beginning of the season would predict membership of longitudinal emotional trajectories across the competitive season. Given that few studies examined the trajectories of various discrete emotions over the competitive season, no specific hypotheses were advanced regarding the number of trajectories, their characteristics (e.g., intercepts) or their evolution through time (e.g., linear and/or quadratic slopes).

Concerning the relationships between ER, EI and longitudinal emotional trajectories, in line with the process model of emotion regulation [START_REF] Gross | The emerging field of emotion regulation: an integrative review[END_REF][START_REF] Gross | Emotion regulation: Conceptual foundations[END_REF], CMRT [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF] and empirical research [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF]Gross & Jones, 2003;[START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF][START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF][START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF]Wagstaff, 2014), we hypothesised that: (a) young athletes reporting higher levels of dispositional EI and reappraisal would be more likely to belong to adaptive longitudinal emotional trajectories; and (b) athletes reporting higher levels of dispositional emotional suppression would be more likely to belong to maladaptive longitudinal emotional trajectories.

Method Participants

A sample of three hundred and twenty-five youth athletes (122 females and 203 males, aged 16.06 years ± 1.59) in intensive training centres participated in the study. Athletes practiced various team (basketball, soccer, rugby, handball, hockey, volleyball) and individual sports (judo, dance, track and field, swimming, boxing, cycling, golf, gymnastics). They participated in international (n = 11), national (n = 219) or regional (n = 95) sport competitions. On average, they were invested in their sport for 8.12 years (± 3.37) and trained 12.38 h/week (± 3.33). Athlete's emotions scores were gathered in time 1 (T1) for 325 participants, in time 2 (T2) for 306 participants and in time 3 (T3) for 226 participants. The main reasons for attrition were disease, training camp, injury, or competition.

Measures

The Multiple Emotions Measure (MEM, [START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF] was used to measure emotions across the three measurement times. The MEM is a French questionnaire composed of eight 4-items subscales measuring anxiety, love, confidence, anger, harmony, happiness, sadness and vitality (Cronbach's α ranged from .76 to .87 for Martinent et al., 2018 and ranged from .74 to .92 for the present study). Participants responded on a 5-point Likert scale with values ranging from 1 (almost never) to 5 (most of the time).

The French version of the Brief Emotional Intelligence Scale (BEIS; Internal consistencies provided by [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF]: X² = 180.78, p < .05, Comparative Fit Index = .91, Root Mean Square Error of Approximation = .06) was used to assess EI at the beginning of the season. This scale, derived from the Emotional Intelligence Scale (EIS; Schutte et al., 1998), is a short and efficient measure of trait-EI [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF]. This questionnaire provides a global score of EI (α = .66 for the present study) using 10 items focusing on appraisal of own emotions, appraisal of others' emotions, regulation of own emotions, regulation of others' emotions, and utilization of emotions. Participants responded on a 7point Likert type scale ranging from 1 (not at all agree) to 7 (completely agree).

The French version (Internal consistencies provided by [START_REF] Christophe | Évaluation de deux stratégies de régulation émotionnelle: la suppression expressive et la réévaluation cognitive[END_REF] (not at all agree) to 7 (completely agree).

Procedure

The present study was conducted according to the international ethical guidelines. The heads of high school and the coaches of the training centres comprised in the study were contacted before the data collection. The athletes' participation was voluntary. A written informed consent was obtained before the beginning of the study. Their parents also informed their contentment. Athletes completed the questionnaires during school courses or in their usual training place. The BEIS and ERQ were completed only at T1 whereas we processed to three measurement times for the MEM (T1, T2, and T3).

Data analyses

LCGAs were conducted on MPlus Version 7.3 (Los Angeles, CA, USA). We used the full information maximum likelihood (FIML) estimation of missing data which is an unbiased and more efficient method under the missing at random assumption than listwise deletion, which could lead to biased parameters [START_REF] Enders | Applied missing data analysis[END_REF]. LCGA is a statistical model which posits that an underlying grouping variable can be inferred from a set of indicators to discover distinct trajectories on a psychological variable with different patterns of change and stability [START_REF] Louvet | Revisiting the changing and stable properties of coping utilization using latent class growth analysis: A longitudinal investigation with soccer referees[END_REF][START_REF] Martinent | Longitudinal trajectories of athlete burnout among young table tennis players: A 3-wave study[END_REF].

As longitudinal factor invariance is a prerequisite when conducting such analyses, preliminary analyses provided evidence for the strict temporal longitudinal invariance of MEM scores, signifying that changes in emotion reflected the real changes of the athletes' emotions over time without a bias interpretation due to temporal non-invariance.

Eight sets of analyses were performed, one for each discrete emotion. First, we conducted a series of unconditional LCGA models to select a model that precisely captured the shape and the number of the trajectories describing each of the emotions). Thus, a succession of models with increasing number of trajectories was achieved to identify which model was associated with the best-fit indices [START_REF] Jung | An introduction to latent class growth analysis and growth mixture modeling[END_REF]. We used a mixture of statistical indices to determine the best-fitting model making up the log-likelihood value, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Adjusted BIC (ABIC), and the Lo, Mendell, and Rubin likelihood ratio test (LRT). The highest log-likelihood scores and the smallest values of AIC, BIC, and ABIC designated the best-fitting model. Initial LCGA models included the mean level (intercept) and the linear and quadratic growths for each trajectory. Then LCGA models with both linear and quadratic functions were compared with their respective LCGA models with only the linear function.

The log likelihood ratio test allowed highlighting an eventual significant improvement of fit if fewer parameters were included in the model (i.e., omitting quadratic functions from LCGA models). Second, we examined whether EI and ER measured at T1 (i.e. beginning of the season) could predict membership of emotional trajectories. Thus, EI and ER scores were included as covariates of the selected models using the three-step model [START_REF] Nylund-Gibson | A latent transition mixture model using the three-step specification[END_REF]. This approach fixes model parameters (intercept, linear and/or quadratic slopes) of the several longitudinal emotional trajectories at the values from the unconditional LCGA models. In other words, three-step models permitted to include the covariates in the mixture models without that covariates alter class estimation [START_REF] Asparouhov | Auxiliary variables in mixture modeling: Three-step approaches using M plus[END_REF].

Results

Unconditional LCGA Models

Results of unconditional LCGAs are presented in Table 1. For love and confidence, we observed big drops of AIC, BIC, and ABIC between 3 and 4 classes. Additionally, LRT revealed that 4 classes fit better than 3 classes while 5 classes did not fit better than 4 classes for introjected regulation. When comparing LCGA models, it is important to consider not only the statistical indicators but also the substantive meaning of each of the emerging trajectories when interpreting the LCGA results [START_REF] Martinent | Longitudinal trajectories of athlete burnout among young table tennis players: A 3-wave study[END_REF]. Thus, to achieve the balance between theoretical and statistical considerations, we used the model parameters to make sense of the classes and decide which model fits best. Based on the interpretability of the emotional trajectories and statistical indicators, we selected a four-class solution for love and confidence. For anger, harmony, happiness, sadness and vitality, the AIC, BIC, ABIC, and LRT indicated that the 3-class models fit best. Specifically, there were big drops between 2 and 3 classes for the AIC, BIC and ABIC. LRT also revealed that 3 classes fit better than 2 classes while 4 classes did not fit better than 3 classes. Based on the interpretability of the emotional trajectories (the three-class solutions made theoretical sense whereas a fourth trajectory did not add anything substantive to the understanding of emotional trajectories) and the LCGA statistical indicators, a three-class solution was selected for anger, harmony, happiness, sadness, and vitality. For the anxiety, there were big drops of AIC, BIC, and ABIC between 1 and 2 classes. Additionally, LRT revealed that 2 classes fit better than 1 class while 3 classes did not fit better than 2 classes for anxiety. A two-class solution was selected for anxiety.

Moreover, it is worth noting that the LRTs indicated significant worsening of fit if quadratic functions were omitted for love (LRT = 27.40, ∆df = 4, p < .001), anxiety (LRT = 5.68, ∆df = 2, p = .05), anger (LRT = 6.80, ∆df = 3, p = .05), happiness (LRT = 13.41, ∆df = 3, p < .005) and sadness (LRT = 12.37, ∆df = 3, p < .01). Thus, both the linear and quadratic parameters were selected for these emotions. However, for confidence, harmony, and vitality (LRT = 6.59, 3.50, and 5.40 respectively, ∆df = 3 or 4, p > .10), the LRT indicated nonsignificant worsening of fit if quadratic functions were omitted. Thus, the linear parameters were only selected for these emotions.

Conditional LCGA Models with Covariates

Interpreting the Emotional Trajectories

The trajectories' estimates for the conditioned LCGA models are presented in Table 2.

For love, the high-and-stable (n = 181) and moderate-and-stable (n = 9) subgroups represented athletes who always experienced high (intercept = 3.86, p < .01, linear = -.08, p = .49, quadratic = .06, p = .21) and moderate (intercept = 3.46, p < .01, linear = -.67, p = .39, quadratic = -.02, p = .96) levels of love across the three time points. The high-and-unstable group (n = 33) referred to high levels of love, a marginally positive linear function and a negative quadratic function (intercept = 3.77, p < .01, linear = .39, p = .08, quadratic = -.37, p < .01) whereas the very-high-and-unstable (n = 102) subgroup described athletes who experienced very high level of love and a positive quadratic function (intercept = 4.47, p < .01, linear = -.15, p = .20, quadratic = .17, p < .01). For confidence, the low-and-increasing (n = 52), the moderate-and-increasing (n = 11) and the high-and-increasing (n = 89) subgroups represented athletes who experienced low (intercept = 2.24, p < .01, linear = .19, p = .05), moderate (intercept = 2.61, p < .01, linear = 1.06, p < .01) and high (intercept = 4.37, p < .01, linear = .15, p = .02) level of confidence (respectively) and a positive linear function. The high-and-decreasing (n = 173) subgroup referred to high level of confidence and a marginally negative linear function (intercept = 3.62, p < .01, linear = -.93, p = .08). For harmony, the high-and-stable (n = 145), moderate-and-stable (n = 166) and low-and-stable (n = 14) subgroups represented athletes who always experienced high (intercept = 4.30, p < .01, linear = -.01, p = .85), moderate (intercept = 3.29, p < .01, linear = .05, p = .32), and low (intercept = 1.88, p < .01, linear = .20, p = .13) levels of harmony across the measurement times. For happiness, the very-high-and-decreasing (n = 101) subgroup referred to very high level of happiness and a negative linear function (intercept = 4.69, p < .01, linear = -.43, p = .01, quadratic = .13, p = .13) whereas the moderate-and-increasing (n = 40) subgroup represented athletes who experienced moderate level of happiness and a positive linear function (intercept = 2.66, p < .01, linear = .64, p = .03, quadratic = -.15, p = .30. The high-and-stable (n = 180) subgroup represented athletes who always experienced high levels of happiness (intercept = 3.72, p < .01, linear = -.13, p = .26, quadratic = .07, p = .25). For vitality, the high-and-stable (n = 133) subgroup represented athletes who always experienced high level of vitality (intercept = 4.66, p < .01, linear = -.06, p = .44) across the season whereas the low-andincreasing (n = 27) subgroup referred to low level of vitality and a positive linear function (intercept = 2.75, p < .01, linear = .40, p = .05). The moderate-and-decreasing (n = 165) subgroup described athletes who experienced moderate level of vitality and a marginally negative linear function (intercept = 3.88, p < .01, linear = -.11, p = .07).

For anxiety, the moderate-and-unstable (n = 130) subgroup represented athletes who experienced moderate level of anxiety, a marginally positive linear function and a marginally negative quadratic function (intercept = 3.08, p < .01, linear = .42, p = .06, quadratic = -.19, p = .09) whereas the low-and-stable (n = 195) subgroup referred to low level of anxiety across the three measurement times (intercept = 1.88, p < .01, linear = .12, p = .49, quadratic = -.09, p = .28). For anger, the moderate-and-stable (n = 93) subgroup represented athletes who always experienced moderate level of anger across the season (intercept = 2.60, p < .01, linear = -.20, p = .59, quadratic = .10, p = .55). The high-and-decreasing (n = 22) subgroup described athletes who experienced high level of anger and a marginally negative linear function (intercept = 3.87, p < .01, linear = -.88, p = .07, quadratic = .27, p = .27) whereas the low-and-unstable (n = 210) subgroup referred to low level of anger, a positive linear function and a negative quadratic function (intercept = 1.37, p < .01, linear = .47, p < .01, quadratic = .18, p = .01). For sadness, the low-and-stable (n = 241) subgroup represented athletes who always experienced low level of sadness (intercept = 1.33, p < .01, linear = .11, p = .44, quadratic = -.03, p = .72. The high-and-unstable (n = 33) subgroup referred to high level of sadness, a negative linear function and a positive quadratic function (intercept = 3.80, p < .01, linear = -1.80, p < .01, quadratic = .68, p < .01) whereas the low-and-unstable (n = 51) subgroup described athletes who experienced low level of sadness, a positive linear function and a negative linear function (intercept = 1.77, p < .01, linear = 2.32, p < .01, quadratic = -.93, p < .01).

Effect of Emotional Intelligence and Emotional Regulation on Membership of

Emotional Trajectories

The logistic regression coefficients highlighting the effects of EI and ER on memberships of emotional trajectories are presented in Table 3 1 . The likelihoods of belonging to the high-and-stable and high-and-unstable trajectories of love -relative to the very-highand-unstable trajectory of love -were negatively related to T1 EI (ORs = .33 and .28 respectively, p s = .02). The likelihoods of belonging to the high-and-decreasing and low-andincreasing trajectories of confidence -relative to the high-and-increasing trajectory of confidence -were negatively related to T1 EI (ORs = .26 and .38, p s = .02 and .06); the likelihoods of belonging to the low-and-increasing trajectory of confidence -relative to the high-and-increasing trajectory of confidence -were negatively related to T1 reappraisal (OR = .64, p = .04). The likelihoods of belonging to the moderate-and-stable trajectory of harmony -relative to the high-and-stable trajectory of harmony -were negatively related to T1 reappraisal (OR = .61, p = .02) and to T1 EI (OR = .14, p < .01). The likelihoods of belonging to the low-and-stable trajectory of harmony -relative to the high-and-stable trajectory of harmony -were negatively related to T1 reappraisal and marginally related to T1 emotional suppression (ORs = .45 and 1.59, p s = .00 and .08). The likelihoods of belonging to the moderate-and-increasing trajectory of happiness -relative to the very-high-and-decreasing trajectory of happiness -were negatively related to T1 reappraisal and to T1 EI (ORs = .37 and .13, p s < .01) and positively related to T1 emotional suppression (OR = .1.73, p = .01).

The likelihoods of belonging to the high-and-stable trajectory of happiness -relative to the very-high-and-decreasing trajectory of happiness -were negatively related to T1 EI (OR = .18, p < .01) and positively related to T1 emotional suppression (OR = .1.41, p = .04). The likelihoods of belonging to the low-and-increasing trajectory of vitality -relative to the highand-stable trajectory of vitality -were negatively related to T1 reappraisal and T1 EI (ORs = .53 and .26, p s = .01). The likelihoods of belonging to the moderate-and-decreasing trajectory of vitality -relative to the high-and-stable trajectory of vitality -were negatively related to T1 reappraisal (OR = .65, p = .01) and positively related to T1 suppression (OR = 1.38, p = .04).

The likelihoods of belonging to the low-and-unstable trajectory of anger -relative to the high-and-decreasing trajectory of anger -were positively related to T1 reappraisal (OR = 1.61, p = .03). The likelihoods of belonging to the low-and-stable trajectory of sadnessrelative to the high-and-unstable trajectory of sadness -were negatively related to T1 emotional suppression (OR = 1.49, p = .02) and positively related to T1 reappraisal (OR = .70, p = .02). The likelihoods of belonging to the low-and-unstable trajectory of sadnessrelative to the high-and-unstable trajectory of sadness -were positively related to T1 reappraisal (OR = 1.68, p = .03).

Discussion

The aim of the present study was to examine whether several subgroups of athletes representing distinct longitudinal emotional trajectories emerged from LCGAs. A wide range of discrete emotions (anger, anxiety, sadness, confidence, happiness, harmony, love, and vitality) were investigated for a sample of young elite athletes involved in intensive training settings followed across 3 measurement points during the competitive season (beginning, middle and end of the sport season). We also explored whether dispositional ER and EI scores reported by youth athletes at the beginning of the season would predict membership of emerging longitudinal emotional trajectories across the competitive season. Results of LCGAs revealed several distinct emotional trajectories for each of the eight discrete emotions. In particular, four trajectories for love (very high and unstable, high and stable, high and unstable, moderate and stable) and confidence (high and increasing, high and decreasing, moderate and increasing, low and increasing), three trajectories for anger (high and decreasing, moderate and stable, low and unstable), harmony (high and stable, moderate and stable, low and stable), happiness (very high and decreasing, high and stable, moderate and increasing), sadness (high and unstable, low and stable, low and unstable) and vitality (high and stable, moderate and decreasing, low and increasing) and two trajectories for anxiety (moderate and unstable, low and stable) emerged from the LCGAs. Thus, the results revealed that each discrete emotion is characterised by a specific degree of interindividual variability.

For instance, in the present study, confidence experiences are particularly heterogeneous as four classes emerged from the LCGAs whereas anxiety experiences could be captured with only two subgroups. As a whole, these results provided evidence for the usefulness of exploring several discrete emotions as distinct trajectories emerging for each of the eight discrete emotions across the competitive season. Indeed, in line with CMRT, results of this study suggested that each specific emotion "tell(s) a different story about a person's adaptational struggle" (Lazarus, 2000, pp. 232).

Confirming the results of [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF] study focusing on positive and negative affective states, those of the present study demonstrated that it is useful to take into account heterogeneity in longitudinal sport emotion across the competitive season for a wide range of pleasant and unpleasant discrete emotions. Indeed, naturally-occurring emotional regulation strategies emerging from the LCGAs highlighted both change (significant linear and/or quadratic slopes) and stability at an intra-individual level of analysis for most of the discrete emotions examined. Albeit with a different methodology, these results are consistent with previous sport studies depicting a nomothetic-idiographic process for the construct of emotion [START_REF] Cerin | Predictors of competitive anxiety direction in male Tae Kwon Do practitioners: A multilevel mixed idiographic/nomothetic interactional approach[END_REF][START_REF] Martinent | A descriptive study of emotional process during competition: Nature, frequency, direction, duration and co-occurrence of discrete emotions[END_REF][START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF]. Specifically, it is important to outline that some but not all athletes have exhibited changes in their experience of discrete emotions across time. For instance, several athletes experienced stable discrete emotions across the three measurement times (beginning, middle, and end of the season) -74% of athletes belonged to stable trajectories for sadness, 60% for anxiety, 58% for love, 55% for happiness, 41% for vitality, and 29% for anger. These athletes do not seem really affected by the intensive training environment and influenced by the competitive or personal events during the season. For harmony, all athletes belonged to stable emotional trajectories (high and stable, moderate and stable, or low and stable). Thus, we could considerate that this discrete emotion has a little temporal variability during a competitive season. In contrast, all the athletes exhibited changes of confidence across the season as the four emerging trajectories for confidence were characterized by linear and/or quadratic changes across time (high and increasing, high and decreasing, moderate and increasing, low and increasing). These changes could refer to the consequences of the athletes' everyday life [START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF]. Indeed, the individual response to the positive and negative competitive events or personal experiences could lead to an increase or a decrease in emotional scores across the season. For instance, the decreasing of positive emotions could be explained by the daily pressure and the biological, psychological and social demands [START_REF] Gaudreau | Trajectories of affective states in adolescent hockey players: Turning point and motivational antecedents[END_REF].

Based on a theoretical background such as the broaden and built theory [START_REF] Fredrickson | The role of positive emotions in positive psychology: The broadenand-build theory of positive emotions[END_REF] and on previous studies in sport psychology [START_REF] Mccarthy | Positive emotion in sport performance: current status and future directions[END_REF][START_REF] Riemer | Development of the athlete satisfaction questionnaire (ASQ)[END_REF], some trajectories could be characterized as maladaptive (i.e. dysfunctional) whereas other could be considered as adaptive (i.e. functional). Because low scores of dispositional unpleasant emotions and high scores of dispositional pleasant emotions have been associated with various positive outcomes (e.g., well-being, performance) and/or an absence of negative ones (e.g., burnout) [START_REF] Beedie | The profile of mood states and athletic performance: Two meta-analyses[END_REF][START_REF] Campo | Emotions in team contact sports: A systematic review[END_REF][START_REF] Martinent | Longitudinal trajectories of athlete burnout among young table tennis players: A 3-wave study[END_REF][START_REF] Mccarthy | Positive emotion in sport performance: current status and future directions[END_REF], it seems reasonable to postulate that the high-and-stable trajectories of love, harmony, vitality and happiness, the moderate-and-increasing and high-and-increasing trajectories of confidence, and the low-and-stable trajectories of anxiety and sadness could be conceptualized as functional emotional trajectories. In contrast, the moderate-and-decreasing trajectory of vitality, low-and-stable trajectory of harmony, moderate-and-unstable trajectory of anxiety and high and unstable trajectory of sadness could be conceptualized as dysfunctional emotional trajectories. For other emotional trajectories (e.g., high-anddecreasing trajectory of anger or low-and-increasing trajectory of confidence), it seems more difficult to definitely characterize these trajectories as functional versus dysfunctional ones.

To unambiguously characterize such emerging emotional trajectories, upcoming research could explore the effect of memberships of emotional trajectories on several distal outcomes (e.g. performance, well-being, burnout) measured at the end of the season or several years later. Nevertheless, the influence of dispositional ER and EI scores assessed at the beginning of the season on membership of emotional trajectories across the competitive season could provide preliminary information about the functional versus dysfunctional nature of emotional trajectories in the context of the present study.

Results of LCGAs revealed that dispositional EI and ER scores at T1 significantly predicted membership of emotional trajectories for love, anger, confidence, harmony, happiness, sadness and vitality. In particular, young elite athletes reporting higher levels of dispositional EI were more likely belonging to functional longitudinal emotional trajectories compared with less functional longitudinal emotional trajectories. These results are in line with previous studies focused on EI [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF][START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF] confirming the positive influence of EI on emotions. Indeed, athletes characterised by high scores of EI at the beginning of the season may use their abilities to overcome the daily constraints of the competitive season. Specifically, in line with CMRT [START_REF] Lazarus | How emotions influence performance in competitive sports[END_REF], a better identification, understanding, regulation and use of emotions may modulate their appraisal of the competitive experiences. These capacities to modify the cognitive evaluation of sport situations may enable athletes to experience functional emotional trajectories during the season. In the same way, athletes reporting higher scores of reappraisal were also more likely belonging to functional longitudinal emotional trajectories confirming previous studies focused on ER [START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF][START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF]. The young athletes who used reappraisal strategies at the beginning of the season may positively change their cognitions about the competitive situations. Thus, the ability to use positive reappraisal strategies seems to promote functional emotional trajectories across the season. In contrast, young elite athletes reporting higher levels of dispositional emotional suppression at T1 were more likely belonging to dysfunctional longitudinal emotional trajectories compared with less dysfunctional emotional trajectories. These results are in line with previous studies highlighting the high cognitive, physiological and social cost of emotional regulation compared with reappraisal (John & Gross, Wagstaff, 2014). Indeed, the recurrence of suppression strategies may disturb the emotional experiences during the season and lead to dysfunctional emotional trajectories. Overall, the results of LCGA are in line with the CMRT confirming the role of EI and ER on cognitive appraisal and emotional experiences in a sport setting [START_REF] Davies | Validity and reliability of a brief emotional intelligence scale (BEIS-10)[END_REF][START_REF] John | Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development[END_REF][START_REF] Laborde | Emotional intelligence in sport and exercise: A systematic review[END_REF][START_REF] Martinent | Athletes' regulation of emotions experienced during competition: A naturalistic video-assisted study[END_REF].

Moreover, the present results highlight the temporal effect of EI and ER at the beginning of the season on emotional trajectories across a competitive season.

These results could be used to help coaches and sport psychologists working with adolescent athletes involved in intensive training centres. The methodological approach used in this study could be useful to identify dysfunctional emotional trajectories and ultimately to help the creation of targeted and adapted intervention to subgroups of young athletes involved in intensive training centres. Knowing which form of ER (reappraisal versus emotional suppression) and whether EI is related to which emotional trajectory is crucial to the development of well-designed prevention and intervention program. Based on the premise that emotional experience can be seen as a nomothetic-idiographic process [START_REF] Cerin | Predictors of competitive anxiety direction in male Tae Kwon Do practitioners: A multilevel mixed idiographic/nomothetic interactional approach[END_REF][START_REF] Martinent | Emotion profiles and their motivational antecedents among adolescent athletes in intensive training settings[END_REF], sport psychologists could try to develop empirically-proven interventions to help young athletes change their dysfunctional emotional trajectories and/or stabilize their functional emotional trajectories over time to maximize their psychological adjustment to the high demands inherent to the environment of intensive training centres.

Based on the LCGA results, interventions should be targeted toward young athletes reporting low dispositional EI and reappraisal and/or high dispositional emotional suppression because these young athletes were found to be the more at risk of developing dysfunctional emotional trajectories across the competitive season. Based on the tripartite model of EI [START_REF] Mikolajczak | Going beyond the ability-trait debate: The three-level model of emotional intelligence an unifying view[END_REF] and on previous empirical studies highlighting that it was possible to increase dispositional EI scores [START_REF] Nelis | Increasing emotional intelligence: (How) is it possible?[END_REF], sport psychologists could implement specific programs designed to enhance young athletes' dispositional EI (e.g., increasing knowledge about emotions and emotion regulation, using pre-competitive and intra-competitive routines; [START_REF] Campo | Emotional intelligence training: Implications for performance and health[END_REF].

The present research has some limitations that should be acknowledged. First, before generalizing the findings, caution should be taken given the specificity of our population (young athletes in intensive training settings). Second, the absence of distal outcomes prevented us to unambiguously examine the functional versus dysfunctional nature of the emerging emotional trajectories. Consequently, future studies should include several outcomes variables in the study design, such as performance or well-being indicators. Third, although a longitudinal design based on the competitive season was used in the present study, sport carrier is longer than this time period. Indeed, the succession of competitive seasons could help to describe other emotional trajectories. In this perspective, future studies should extend the knowledge base about the longitudinal development of emotions in the sport context in focusing on four competitive seasons (representing an Olympiad). Moreover, it is noteworthy that EI and ER represent particular predictive factors in a multifactorial system (e.g. motivation, training history) and it could be useful to explore the influence of other factors in future studies to provide further insights on emotional experience in a sport context. Despite these limitations, the present study furthered our knowledge base on sport emotions by providing new insights on longitudinal everyday experience of eight discrete emotions (love, anxiety, anger, confidence, harmony, happiness, sadness and vitality) with a sample of young elite athletes involved in intensive training centres followed across 3 measurement points during the competitive season (beginning, middle and end of the sport season). The person-centred method used in the present study highlighted that the heterogeneity in longitudinal sport emotions needs to be accounted for to identify intra-individual emotional trajectories experienced by athletes through the competitive season.

Moreover, results of LCGAs also demonstrated that dispositional EI and ER assessed at the beginning of the season significantly predicted membership of longitudinal emotional trajectories across the competitive season. In particular, EI and reappraisal predicted belonging to functional emotional trajectories whereas emotional suppression predicted belonging to dysfunctional emotional trajectories. These findings are interesting for sport psychology because they help to target particular athletes at risk of developing dysfunctional emotional trajectories across the competitive season.

Conflicts of interest

All the authors declare that they have no conflict of interest.

  : X² = 180.78, p < .05, Comparative Fit Index = .90, Root Mean Square Error of Approximation = .05) of the Emotional Regulation Questionnaire (ERQ, Gross & John, 2003) was used to measure dispositional reappraisal (α = .79 for Gross & John, 2003; α = .83 for the present study; 6 items) and emotional suppression (α = .73 for Gross & John, 2003; α = .72 for the present study; 4 items). Participants responded on a 7-point Likert type scale ranging from 1

Table 2 .

 2 Longitudinal Trajectories of Emotions across the 3 Waves.

			Intercept		Linear		Quadratic
		N Estimate (SE) p Estimate (SE) p Estimate (SE) p
	Love
	High and stable	181	3.86 (.08)	.00	-0.08 (.11)	.49	0.06 (.05)	.21
	Very high and unstable	102	4.47 (.05)	.00	-0.15 (.12)	.20	0.17 (.06)	.00
	High and unstable	33	3.77 (.14)	.00	0.39 (.22)	.08	-0.37 (.10)	.00
	Moderate and stable	9	3.46 (.32)	.00	-0.67 (.78)	.39	-0.02 (.33)	.96
	Anxiety
	Moderate and unstable	130	3.08 (.18)	.00	0.42 (.22)	.06	-0.19 (.11)	.09
	Low and stable	195	1.88 (.09)	.00	0.12 (.17)	.49	-0.09 (.09)	.28
	Anger
	Moderate and stable	93	2.60 (.17)	.00	-0.20 (.38)	.59	0.10 (.16)	.55
	High and decreasing	22	3.87 (.21)	.00	-0.88 (.49)	.07	0.27 (.25)	.27
	Low and unstable	210	1.37 (.04)	.00	0.47 (.13)	.00	-0.18 (.06)	.01
	Confidence
	High and decreasing	173	3.62 (.10)	.00	-0.93 (.05)	.08
	Low and increasing	52	2.24 (.15)	.00	0.19 (.10)	.05
	High and increasing	89	4.37 (.09)	.00	0.15 (.06)	.02
	Moderate and increasing	11	2.61 (.43)	.00	1.06 (.10)	.00
	Harmony
	High and stable	145	4.30 (.08)	.00	-0.01 (.05)	.85
	Moderate and stable	166	3.29 (.09)	.00	0.05 (.05)	.32
	Low and stable	14	1.88 (.17)	.00	0.20 (.13)	.13
	Happiness
	Very high and decreasing 101	4.69 (.06)	.00	-0.43 (.17)	.01	0.13 (.08)	.13
	Moderate and increasing	40	2.66 (.13)	.00	0.64 (.30)	.03	-0.15 (.15)	.30
	High and stable	180	3.72 (.06)	.00	-0.13 (.11)	.26	0.07 (.06)	.25
	Sadness
	Low and stable	241	1.33 (.04)	.00	0.11 (.14)	.44	-0.03 (.07)	.72
	High and unstable	33	3.80 (.14)	.00	-1.80 (.35)	.00	0.68 (.17)	.00
	Low and unstable	51	1.77 (.15)	.00	2.32 (.51)	.00	-0.93 (.27)	.00
	Vitality
	High and stable	133	4.66 (.05)	.00	-0.06 (.07)	.44
	Low and increasing	27	2.75 (.24)	.00	0.40 (.20)	.05
	Moderate and decreasing 165	3.88 (.10)	.00	-0.11 (.06)	.07

Table 3 .

 3 Association of Time 1 (T1) Emotional Regulation and Emotional Intelligence with Trajectories of Emotions.

	Model

We also computed a series of LCGA model in which EI, reappraisal, emotional suppression, and the interaction terms of EI x reappraisal and EI x emotional suppression were simultaneously included as predictors of emotional trajectories. Results revealed no significant results for the interaction terms. Therefore, the interaction terms were not included within the final conditional LCGA models.

Funding

This research did not receive any specific grant from funding agencies.

Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008)