Tiphaine Moussilmani

Sophie Knight

Julien Mancini

Cyril Touboul

Florence Rodriguez

Pierre Adrien Bolze

Sofiane Bendifallah

Marcos Ballester

Pierre Collinet

Yohan Kerbage

V Lavoue

MD Xavier Carcopino

Yohann Dabi

MD Pr Xavier Carcopino

Lobna Ouldamer

Geoffroy Atrous

Vincent Lavoué

Ludivine Dion

Emilie Raimond

Olivier Graesslin

Cyrille Huchon

Myriam Mimouni

Alexandre Bricou

François Golfier

Xavier Carcopino1

Prognosis impact of posttreatment pelvic MRI in patients treated for stage IB2-IIB cervical cancer with chemoradiation therapy

ABSTRACT

Objectives: To evaluate the performances of systematic posttreatment pelvic magnetic resonance imaging (PPMRI) in predicting prognosis of patients treated with chemoradiation therapy (CRT) for locally advanced cervical cancer (LACC).

Materials and methods:

Multi-institutional data from 216 patients presenting FIGO IB2-IIB cervical cancer for which PPMRI was performed following CRT were retrospectively reviewed. Incomplete response was defined as the identification of persistent lesion on PPMRI. Primary endpoints were patients' 5-year recurrence free (RFS) and overall (OS) survivals. Secondary endpoint was the identification of residual histologic disease on hysterectomy specimens when completion surgery was performed.

Results: PPMRI identified an incomplete response in 102 (47.2%) cases. A 70% or more reduction in tumor size on PPMRI was identified as the best predictive cut-off for recurrence (37.7% sensitivity and 78.7% specificity) and death (50% sensitivity and 77.9% specificity) with significant impact on those risks (HRa: 0.42; 95%CI: 0.23-0.77 and HRa: 0.18; 95%CI: 0.06-0.50, respectively). Completion hysterectomy was performed in 117 (54.4%) cases, with histologic residual disease in 55 (47.4%). PPMRI demonstrated 74.5% sensitivity and 50.8% specificity in predicting residual disease. Although survival of patients with complete response at PPMRI was not impacted by completion hysterectomy, it significantly increased 5-year RFS and OS of those with incomplete response: 38.7% vs. 65.3% (p<0.001) and 63% vs. 82.9% (p=0.038), respectively.

Conclusion:

A 70% or more reduction of in tumor size on PPMRI following CRT in patients with LACC is predictive of RFS and OS. PPMRI could help triaging patients who could benefit from completion hysterectomy.

J o u r n a l P r e -p r o o f

Keywords: Pelvic MRI; cervical cancer; prognosis; survival; recurrence; chemoradiation J o u r n a l P r e -p r o o f INTRODUCTION With 569 847 new cases and 311 365 deaths reported in 2018, cervical cancer represents a genuine worldwide health challenge and the fourth cancer among women [START_REF] Bray | Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[END_REF]. It remains a dramatic disease since it affects young women with high mortality. Although surgery is commonly performed for the management of microinvasive cervical cancer and early FIGO stages, combined chemoradiation therapy (CRT) has been demonstrated as the treatment of choice for patients diagnosed with locally advanced disease [START_REF] Green | Concomitant chemotherapy and radiation therapy for cancer of the uterine cervix[END_REF]. Such combined therapy is now considered as a standard as it was proven to improve overall and progression-free survival and to reduce local and distant recurrences [START_REF] Green | Survival and recurrence after concomitant chemotherapy and radiotherapy for cancer of the uterine cervix: a systematic review and meta-analysis[END_REF][START_REF] Landoni | Randomised study of radical surgery versus radiotherapy for stage Ib-IIa cervical cancer[END_REF][START_REF]Reducing uncertainties about the effects of chemoradiotherapy for cervical cancer: individual patient data meta-analysis[END_REF]. However, despite obvious efficacy of CRT, recurrences are unfortunately still common, occurring mostly within the first two years of follow-up [START_REF] Yoon | Validation of Nomograms for Survival and Metastases after Hysterectomy and Adjuvant Therapy in Uterine Cervical Cancer with Risk Factors[END_REF][START_REF] Rose | Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic J o u r n a l P r e -p r o o f Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy[END_REF]. Additionally to the FIGO staging, pelvic and para aortic nodal status and tumor dimensions have been identified as major initial prognosis factors [START_REF] Rose | Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic J o u r n a l P r e -p r o o f Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy[END_REF][START_REF] Beriwal | Complete metabolic response after definitive radiation therapy for cervical cancer: patterns and factors predicting for recurrence[END_REF]. Another key prognosis factor is the identification of posttreatment cervical residual disease. However, the proper evaluation of patients' response to CRT remains challenging as solely the histologic analysis of surgical specimen from patients undergoing completion hysterectomy can reliably asses this parameter.

Pelvic magnetic resonance imaging (MRI) has been shown to be the best imaging examination for the initial staging of patients diagnosed with cervical cancer [START_REF] Bhatla | Cancer of the cervix uteri[END_REF][START_REF] Bhatla | Revised FIGO staging for carcinoma of the cervix uteri[END_REF]. It provides precise characterization of the disease with the ability to determine the exact tumor dimensions and its local spread with possible involvement of nearby organs such as parameter and ureters, vagina, bladder and rectum [START_REF] Balleyguier | Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology[END_REF][START_REF] Mitchell | Early invasive cervical cancer: tumor delineation by magnetic resonance imaging, computed tomography, and clinical examination, verified by pathologic results, in the ACRIN 6651/GOG 183 Intergroup Study[END_REF]. Pelvic MRI is also commonly performed for the evaluation of tumor's response following CRT. However, its performances in this precise indication remains questionable. Although previous studies have shown its ability to predict patients' outcome and survival, there is a lack of evidence regarding the ability of J o u r n a l P r e -p r o o f posttreatment pelvic magnetic resonance imaging (PPMRI) to predict histological cervical residual disease. Thus, only few studies have correlated PPMRI results with the histological analysis of hysterectomy specimens in patients who had undergone completion surgery [START_REF] Gui | Cervical cancer response to neoadjuvant chemoradiotherapy: MRI assessment compared with surgery[END_REF][START_REF] Ferrandina | Can radicality of surgery be safely modulated on the basis of MRI and PET/CT imaging in locally advanced cervical cancer patients administered preoperative treatment?[END_REF][START_REF] Pinkavova | Transrectal ultrasound and magnetic resonance imaging in the evaluation of tumor size following neoadjuvant chemotherapy for locally advanced cervical cancer[END_REF][START_REF] Hequet | Evaluation and impact of residual disease in locally advanced cervical cancer after concurrent chemoradiation therapy: results of a multicenter study[END_REF][START_REF] Vincens | Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2/II cervical carcinoma with chemoradiation therapy : correlation of radiologic findings with surgicopathologic results[END_REF]. Additionally, these retrospective studies suffer from limited power with little number of patients included, ranging from 41 to 159 only [START_REF] Gui | Cervical cancer response to neoadjuvant chemoradiotherapy: MRI assessment compared with surgery[END_REF][START_REF] Ferrandina | Can radicality of surgery be safely modulated on the basis of MRI and PET/CT imaging in locally advanced cervical cancer patients administered preoperative treatment?[END_REF][START_REF] Pinkavova | Transrectal ultrasound and magnetic resonance imaging in the evaluation of tumor size following neoadjuvant chemotherapy for locally advanced cervical cancer[END_REF][START_REF] Hequet | Evaluation and impact of residual disease in locally advanced cervical cancer after concurrent chemoradiation therapy: results of a multicenter study[END_REF][START_REF] Vincens | Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2/II cervical carcinoma with chemoradiation therapy : correlation of radiologic findings with surgicopathologic results[END_REF].

The possibility to identify post-RCT residual disease represents a genuine challenge as it could allow for tailored surgery in selected patients. To date, the benefit of completion surgery following CRT in locally advanced cervical cancer remains widely debated as it has not shown any improvement in patients' survival and is associated with genuine morbidity [START_REF] Azria | Results of hysterectomy in patients with bulky residual disease at the end of chemoradiotherapy for stage IB2/II cervical carcinoma[END_REF][START_REF] Chereau | The role of completion surgery after concurrent radiochemotherapy in locally advanced stages IB2-IIB cervical cancer[END_REF]. Previous data however suggest that completion surgery could improve local control in patients with partial pathological response [START_REF] Classe | Surgery after concurrent chemoradiotherapy and brachytherapy for the treatment of advanced cervical cancer: morbidity and outcome: results of a multicenter study of the GCCLCC (Groupe des Chirurgiens de Centre de Lutte Contre le Cancer)[END_REF][START_REF] Carcopino | Equivalent survival in patients with advanced stage IB-II and III-IVA cervical cancer treated by adjuvant surgery following chemoradiotherapy[END_REF][START_REF] Houvenaeghel | Long-term survival after concomitant chemoradiotherapy prior to surgery in advanced cervical carcinoma[END_REF][START_REF] Lèguevaque | Completion surgery or not after concurrent chemoradiotherapy for locally advanced cervical cancer?[END_REF]. Finally, completion surgery allows for the identification of patients with incomplete histologic response and higher risk of recurrence that should be submitted to adjuvant therapies [START_REF] Ferrandina | Preoperative concomitant chemoradiotherapy in locally advanced cervical cancer: safety, outcome, and prognostic measures[END_REF][START_REF] Morice | The role of surgery after chemoradiation therapy and brachytherapy for stage IB2/II cervical cancer[END_REF].

The aim of this study was to evaluate the performances of systematic PPMRI in predicting prognosis of patients treated with CRT for locally advanced cervical cancer. We also evaluated the ability of PPMRI in predicting cervical histologic residual disease.

METHODS

Study population

Data from all patients treated for stage IB2 to IIB cervical cancer by chemoradiation therapy (CRT) in 9 French institutions from April 1996 to May 2016, and for which a systematic PPMRI was performed were retrospectively analyzed. Patients' initial characteristics, therapeutic management and follow up were retrieved from medical charts. Primary endpoint was patients' survival. Secondary endpoint was the histologic identification of residual J o u r n a l P r e -p r o o f disease on hysterectomy specimens when completion surgery was performed. Informed consent was obtained from all participants. The study protocol received ethical approval from the Institutional Review Board of the Collège National des Gynécologues et Obstétriciens Français (CEROG 2016-GYN-0502).

Patients' pretreatment evaluation included physical examination, cervical biopsy and/or diagnostic conization completed with PET/CT and pelvic MRI. The FIGO staging was established based on the results of physical examination combined with pelvic MRI according to the 2009 FIGO classification [START_REF] Pecorelli | Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium[END_REF]. The retrospective nature of the study did not allow for the use of the revised 2019 FIGO classification for cervical cancer [START_REF] Bhatla | Cancer of the cervix uteri[END_REF]. The tumor maximal size was determined on pre-therapeutic pelvic MRI and corresponded to the largest dimension of the tumor. When indicated, initial surgical nodal staging including pelvic and/or para-aortic laparoscopic lymphadenectomy was performed. The patients' lymph node status was therefore classified according to three categories depending of the findings of PET/CT and/or surgical nodal staging when performed. Patients were considered as node positive (N+) when nodal involvement was identified on the pre-therapeutic PET/CT and/or by the surgical nodal staging. Patients were considered as node negative (N-) only after negative surgical nodal staging regardless of the results of the imaging. Finally, patients with negative pre-therapeutic PET/CT but who had not undergone surgical nodal staging or who had had neither pretreatment PET/CT nor surgical nodal staging were considered as unknown (Nx).

Treatment modalities were established by a local multidisciplinary committee according to the French national guidelines. Although all patients did receive CRT, they could either have received additional vaginal brachytherapy (VBT) and/or completion hysterectomy. Thus, the following four treatment strategies were recorded: exclusive CRT; CRT followed by VBT; CRT followed by completion surgery; CRT followed by VBT and completion surgery. The decision whether to perform completion surgery (i.e. hysterectomy) solely depended on the J o u r n a l P r e -p r o o f practice of each participating institution, as some systematically did perform completion hysterectomy whilst others did not, and some considered completion surgery only in cases with documented residual disease following CRT.

PPMRI was performed within a 3 month delay following the completion of CRT or after additional VBT when performed. An incomplete response at PPMRI was defined as the identification of persistent cervical and/or nodal lesion. Cervical and nodal incomplete response were defined as a persistent cervical lesion and persistent nodal lesion, respectively. Systematic follow-up included visits every 3 months during the first 2 years, every 6 months during the 3 following years and annually after 5 years. Follow-up included physical examination while imaging including PET/CT and/or pelvic MRI was performed in case of suspected recurrence.

Statistical analysis:

5-year recurrence-free (RFS) and overall (OS) survivals were estimated. RFS and OS were defined as the duration from the date of primary treatment to recurrence and death, respectively. In case no event was reported, they were censored at the date of last follow-up. RFS and OS were estimated for the following variables: age, BMI, parity, menopausal status, FIGO stage, histology, tumor size, nodal status, treatment modality and results of PPMRI.

Identification of optimal cut-off in the reduction of cervical tumour's dimension as predictive factor for RFS and OS was performed using the receiver operator characteristics (ROC) curve analysis. Multivariate analysis was conducted including variables that were identified as significant RFS and OS prognostic factors in our study and in literature.

Results of PPMRI were compared to the presence or absence of histologic residual disease on hysterectomy specimens when completion surgery had been performed. PPMRI diagnostic performances for histologic residual disease were evaluated using sensitivity and specificity.

J o u r n a l P r e -p r o o f

Patient characteristics were reported using counts (%) for categorical variables and mean ± standard deviation for continuous variables. Chi2 statistics were used to compare indicators of diagnostic performances. Kaplan-Meier estimates were used to estimate the event-time distributions, and log-rank test was used to compare the differences among the different groups in terms of RFS and OS. Hazard Ratios (HR) in univariate analysis and adjusted Hazard ratios (HRa) in multivariate analysis were estimated using Cox model. A p value of <0.05 was considered statistically significant. Statistical analysis was performed using IBM SPSS Statistics version 20.0 (IBM Inc., New York, NY, USA).

RESULTS

Patients

A total of 216 patients with stage IB2 to IIB cervical cancer who received CRT and for which a systematic posttreatment pelvic MRI had been performed were included in the study.

Patients' characteristics are reported in Table 1. An incomplete response was identified on PPMRI in 102 (47.2%) cases. Completion surgery was performed in 117 (54.4%) cases, with histologic residual disease identified in 55 (47.4%) cases, mean size of histologic residual disease was 6 mm (±11.0). Median duration of follow-up was 39.2 months (95%CI: 32.4-46).

The 5-year RFS and OS were respectively 60% (95%CI: 53-69) and 82% (95%CI: 75-90).

J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Predictive factors for recurrence

The 5-year RFS for patients with an incomplete response at PPMRI was 58.4% vs 61.9% for patients with complete metabolic response (p=0.55) (Figure 1A). Although the identification of incomplete response on PPMRI was not found to significantly impact the RFS, the identification of histologic cervical residual disease in women who had undergone completion hysterectomy was significantly associated with an increased risk of recurrence (HR: 3.40; 95% CI: 1.6-7.4; p=0.002). Other factor identified to significantly impact the risk of recurrence was nodal status, with an increased risk of recurrence of Nx and N+ patients when compared to N-(HRa: 2.25; 95%CI: 1.19-4.28; p=0.01 and HRa: 2.340; 95%CI: 1.21-4.76; p=0.01, respectively). Compared to patients who were solely treated with CRT, only patients who received additional VBT followed by completion hysterectomy demonstrated a reduction in the risk of recurrence (HRa: 0.33; 95%CI: 0.14-0.81; p=0.02) (Table 2). Considering the reduction in tumor's size observed when comparing pre and PPMRI, best prediction in patients' RFS was found for a threshold of 70% reduction or more (37.7% sensitivity and 78.7% specificity). Thus, compared to others, a reduction ≥70% in tumor's size was found to significantly reduce the risk of recurrence (HRa: 0.42; 95%CI: 0.23-0.77; p=0.005) (Table 2 J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Predictive factors for death

The 5-year OS for patients with an incomplete response at PPMRI was 78.4% vs. 84.6% for patients with a complete response (p=0.047) (Figure 1B). An incomplete response at PPMRI was not identified as a significant risk factor of death (Table 3). The best prediction in patients' OS was found for the same threshold of 70% reduction or more in tumor's size with a 50% sensitivity and 77.9% specificity. Thus, a reduction ≥70% in tumor's size was the only factor identified to significantly prevent the risk of death (HRa: 0.18; 95%CI: 0.06-0.50; p=0.001) (Table 3). J o u r n a l P r e -p r o o f

Diagnostic performances of PPMRI in predicting cervical residual disease

When only considering the 117 patients who had undergone completion surgery, MRI sensitivity and specificity in predicting histologic cervical residual disease was 75% (95% CI: 63-86) and 51% (95%CI: 38-63), respectively (Table 4). MRI diagnostic performances were not impacted by histology, with comparable results in patients with squamous cervical cancers and others. Only the size of the histologic cervical residue was found to impact the diagnostic performances of PPMRI. Diagnostic performances were better when the size of histologic residual disease was ≥10 mm, with 89.7 % sensitivity vs. 50.0% for smaller residues (p=0.005). All values are expressed as n (%)

Cervical residual disease on hysterectomy specimen

Ability of PPMRI to select patients who could benefit from completion surgery

While survival of patients found to have complete response at PPMRI was not impacted by the practice of completion surgery, significant increase in RFS and OS was observed in patients who had undergone completion surgery following the diagnosis of incomplete response at posttreatment PMRI. Thus, among these patients, 5-year RFS was of 65.3% when completion surgery was performed vs 38.7% when not performed (p<0.001); 5-years OS was 82.9% vs 63% (p=0.038), respectively (Figure 2).

J o u r n a l P r e -p r o o f Our results suggest PPMRI to be a suitable tool for triaging patients that could benefit from completion surgery. Thus, although patients with PPMRI complete response did not show any benefit in undergoing completion surgery, it significantly improved both RFS and OS in patients with incomplete response. The debate about whether or not completion surgery should be performed following CRT has been ongoing for decades. At this stage, available published data reported no obvious benefit of completion surgery but genuine morbidity [START_REF] Azria | Results of hysterectomy in patients with bulky residual disease at the end of chemoradiotherapy for stage IB2/II cervical carcinoma[END_REF][START_REF] Classe | Surgery after concurrent chemoradiotherapy and brachytherapy for the treatment of advanced cervical cancer: morbidity and outcome: results of a multicenter study of the GCCLCC (Groupe des Chirurgiens de Centre de Lutte Contre le Cancer)[END_REF][START_REF] Classe | Surgery after concurrent chemoradiotherapy and brachytherapy for the treatment of advanced cervical cancer: morbidity and outcome: results of a multicenter study of the GCCLCC (Groupe des Chirurgiens de Centre de Lutte Contre le Cancer)[END_REF][START_REF] Lèguevaque | Completion surgery or not after concurrent chemoradiotherapy for locally advanced cervical cancer?[END_REF]. Our results are consistent with this statement as the sole practice of completion surgery did not demonstrate any improvement in patients risk of recurrence, nor death (tables 2 and 3). Previous data suggest that completion surgery could be beneficial in selected patients with documented post RCT residual cervical disease and of no therapeutic impact in patients with complete response following CRT [START_REF] Chereau | The role of completion surgery after concurrent radiochemotherapy in locally advanced stages IB2-IIB cervical cancer[END_REF][START_REF] Morice | Results of the GYNECO 02 study, an FNCLCC phase III trial comparing hysterectomy with no hysterectomy in patients with a (clinical and radiological) complete response after chemoradiation therapy for stage IB2 or II cervical cancer[END_REF]. Such approach is consistent with our findings. Although our results suggest completion surgery does not improve outcomes of patients that achieved complete response at PPMRI, this option seems genuinely worthwhile in selected cases showing incomplete response and should therefore be considered.

With a 70% threshold in the reduction of the initial dimension of the lesion identified as the best predictor for outcome, our findings raise concerns regarding the possibility of timerelated ongoing effects of CRT. Indeed, as PPMRI were performed within a 3 month delay following the completion of CRT, it is possible that patients had achieved complete histologic J o u r n a l P r e -p r o o f response after PPMRI had been performed. This could at least partially explain why, among 71 patients found with incomplete response on PPMRI, 30 (42.3%) finally showed no histologic residue on hysterectomy specimen (Table 4). This hypothesis is supported by previous published data showing that early MRI evaluation of cervical cancer's response to CRT is less reliable if performed too early, with increased risk of false positive results when performed before a 3 months delay [START_REF] Gui | Cervical cancer response to neoadjuvant chemoradiotherapy: MRI assessment compared with surgery[END_REF][START_REF] Vincens | Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2/II cervical carcinoma with chemoradiation therapy : correlation of radiologic findings with surgicopathologic results[END_REF]. The delay in which imaging exams should be performed following CRT remains widely discussed. To date, there is still no consensus about the optimal time delay when PPMRI should be performed following RCT and practice was shown to vary from 3 weeks to 6 months after completion of CRT across Europe [START_REF] Balleyguier | Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology[END_REF]. As morbidity of completion surgery directly depends on the delay following CRT and increases with time, it is however crucial that imaging should be performed early enough to enable surgery to be considered and organized. Unfortunately, the exact date when PPRMI had been performed were not properly documented in a majority of patients' medical charts, therefore not allowing for a precise estimation of the impact of delay following CRT on the prediction of histologic residual disease. We believe this point to be of major importance when interpreting our results and considering exporting those to clinical routine practice as our results cannot be extrapolated to PPMRI performed after the delay of 3 months reported here.

Additionally to the possible time related delay in CRT effect, our results suggest performance of PPMRI to directly depend on the dimensions of cervical residual disease. Thus, only histologic residue of 10 mm or more are likely to be properly identified by PPMRI. This finding provides additional information on the possible reasons and risks of false negative results of PPMRI. Due to the retrospective nature of our analysis, we were not able to provide reliable information regarding MRI types and characteristics, precise MRI sequences used and imaging evaluation. We acknowledge this point to represent a major limitation that should be carefully considered when interpreting our results. It is thus unfortunate that we were not able J o u r n a l P r e -p r o o f to identify the impact of diffusion-weighted PPMRI on its performance and to assess values of changes in cervical tumour's apparent diffusion coefficient as this data was not systematically recorded. Because it showed high value for the evaluation of tumour viability and prediction of treatment efficacy, this technique is currently routinely used in all our participating centres for PPMRI evaluation [START_REF] Feng | Combined dynamic DCE-MRI and diffusion-weighted imaging to evaluate the effect of neoadjuvant chemotherapy in cervical cancer[END_REF][START_REF] Ho | Volumetric assessment of apparent diffusion coefficient predicts outcome following chemoradiation for cervical cancer[END_REF][START_REF] Gu | Prognostic value of ADC quantification for clinical outcome in uterine cervical cancer treated with concurrent chemoradiotherapy[END_REF][START_REF] Schreuder | Monitoring treatment response in patients undergoing chemoradiotherapy for locally advanced uterine cervical cancer by additional diffusion-weighted imaging: A systematic review[END_REF][START_REF] Harry | Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer[END_REF]. With our retrospective analysis running for a 20 year period, only most recent PPMRI examinations from our cohort were likely to have used diffusion-weighted technique. Although we were not able to specifically assess the benefit of this technique, we did not observe any significant change of PPMRI prognosis value over time when assessing the impact of time on patients' outcome.

CONCLUSIONS

Systematic PPMRI is predictive of the prognosis of patients treated with CRT for locally advanced cervical cancer. Best prediction in patients' prognosis was achieved when a reduction of 70% or more in the dimensions of the lesion was observed. Finally, PPMRI demonstrated high sensitivity in predicting cervical histologic residual disease, with optimal prediction achieved for histologic residual disease of at least 10 mm. It therefore could help for the identification of patients with histologic residual disease and therefore for the triage of patients that could benefit from completion surgery. Thus, in patients with IB2-IIB cervical cancer, although the identification of complete response at PPRMI performed within 3 months after CRT does not support the practice of completion surgery, it should be considered in patients with incomplete response.

J o u r n a l P r e -p r o o f

Figure 1 .

 1 Figure 1. Recurrence-free (A) and overall (B) survivals based on posttreatment PMRI results

Figure 2 .

 2 Figure 2. Recurrence-free (A) and overall (B) survivals in cases of incomplete response on posttreatment PMRI depending on whether completion surgery was performed or not.

Table 1 .

 1 Patients' characteristics (n=216) Values are expressed as n (%), unless otherwise indicated ‡ Adenocarcinomas (n=27) and other histology type (n=7) *Defied as follows: N+ (N+ on PET/CT or N-on PET/CT but N+ after surgical nodal staging or N+ after surgical

	Age, mean (± SD) (years)

Table 2 .

 2 Identification of prognostic factors of recurrence.

	)

HR: Hazard Ratio; HRa: Adjusted Hazard; BMI: Body Mass Index; CRT: chemoradiation therapy; PPMRI: posttreatment pelvic MRI. * Compared to IB2 stage (reference) ** Compared to other histology type † Compared to N-patients (reference) ‡ Compared to patients treated with exclusive CRT (reference)

Table 3 .

 3 Identification of prognostic factors of death.

	HR (95 % CI)	p	HRa (95 % CI)	p

** Compared to other histology types † Compared to N-patients (reference) ‡ Compared to patients treated with exclusive CRT (reference)

Table 4 .

 4 Diagnostic performances of posttreatment pelvic MRI in predicting documented histologic cervical residual disease in completion hysterectomy specimens after CRT.

	No	Yes	Total

J o u r n a l P r e -p r o o f

Conflict of Interest' statement: All authors have no conflict of interest to declare.

J o u r n a l P r e -p r o o f


Ma Alamilla-Daniel

Richard Moreau

Redarce Tanneguy

Enhanced tracking wall: A real-time computing method for needle injection on haptic simulators

Haptic simulators can help medical students to train and improve their skills before practicing with a real patient. However, the vast majority of needle insertion haptic simulators are based on sophisticated models that are accurate but highly demanding in computing resources. Most of them do not provide haptic feedback and/or are not suitable for haptic control due to their computing time. In this paper, we presented a new low computing consuming method that aims to provide a realistic needle insertion experience to the student. A description of the proposed solution is provided, and it is illustrated by experimental results to highlight its performance.

I. INTRODUCTION

Needle injection is one of the most used procedures that doctors must face day by day. In rheumatology, needle injection can be used to relieve pain in the joints, introduce drugs, and carry out biopsies. During this procedure, doctors have the possibility to use an echographic probe to follow the needle position inside the joint. The difficulty of this gesture is to handle both tools (needle and echographic probe) which requires a visual-motor coordination. To train, medical students can use corpses or manikins before practicing on real patients. However, this kind of training has several drawbacks such as:

• It is not customizable to represent different kinds of patients. • It is not realistic enough from a haptic point of view.

• It is not repeatable.

• It does not allow to record the full gesture (position, force, etc.). Thanks to technological advances, haptic simulators have become a way to help doctors to improve and gain medical's skills before practicing on real patients. These simulators allow working in different environments with force feedback to help immerse them in diverse situations. It also reduces the time spent on patients to complete the training and allows them to extrapolate their acquired skills to a reallife situation. However, haptic simulators still present some difficulties to simulate the different inherent stiffness of the body such as skin, fat, muscles and bones. Even though the involved forces can be calculated using biomechanical models, which are mostly based on FEM (Finite Element Method), their implementation in real-time can be limited due to the high computing resources needed to compute forces. [START_REF] Kurita | Haptic rendering of a needle insertion by enhancing the real force response of a base object[END_REF]. richard.moreau@insa-lyon.fr

In this paper, we introduce an enhancement of the previous "Tracking Wall" method introduced in [START_REF] Alamilla | A new method to render virtual walls for haptic systems: Tracking wall. Application to needle insertion simulation[END_REF]. This new method can compute the forces that act on the needle during the insertion into the tissue along the axial direction (cutting forces and attracted forces). This method is also implemented with a virtual fixture to increase the realism of the operation. We also present the results of the experimental validation of this method and our future perspectives for a more complete simulator.

II. STATE OF ART

To develop an injection simulator, the main issue is to give realistic force feedback of the tissue deformation to the final user. In the bibliography several methods and techniques are available. Most of them are based on the use of mathematical biomechanical models, which are high resource-demanding, and othersonly mimic in a basic way the feeling of deforming the patient's tissue.

With haptic devices, it is possible to create different virtual environments like objects/wall collision scenarios. This can be done by controlling the desired force applied by the device on the hand of the user, which is the simplest method called direct method. The forces and torques are generated to give the sensation of interaction by computing the distance between the tool and the surface [START_REF] Ryden | A proxy method for real-time 3-DOF haptic rendering of streaming point cloud data[END_REF]. In practice, this solution is not suitable, as chattering problems can occur on the tip of the tool due to the sudden change of applied force perturbing the haptic rendering.

One method to render forces in the virtual environment is using the God-Object algorithm, also known as a surface contact point, proxy point, or IHIP (Ideal Haptic Interface Point) [START_REF] Basdogan | Haptic Rendering in Virtual Environments[END_REF], which is implemented in several haptic simulators [START_REF] Zilles | A constraint-based god-object method for haptic display[END_REF]. This algorithm begins with the assumption that it is not possible to avoid that the user overpasses the virtual location of a surface in the real world. This is due to the in-exactitude of the human body to sensor small forces from the haptic device. To avoid unreal behaviour, the algorithm uses a virtual point called the God-Object, which is located at the edge of the virtual object. The forces are rendered using impedance control techniques based on the relative position of the god object against the real tool.

Another way to render the insertion forces is to implement Voigt model [START_REF] Brett | Simulation of resistance forces acting on surgical needles[END_REF]. This method uses a virtual spring and a damper in parallel, which is are very low computer consuming, but inaccurate.

The use of FEM is widely spread due to its accuracy in calculating the behavior of the tissue during puncture or deformation. One example is the method presented by [START_REF] Yang | Interaction modeling and simulation of a flexible needle insertion into soft tissues[END_REF] where authors used a tetrahedral mesh to successfully compute the reaction forces in the needle while injecting. However, FEM is very well known for its highly demanding resources, which impedes by the moment, being applied in online estimations for haptic simulators.

In [START_REF] Podder | Needle insertion force estimation model using procedure-specific and patient-specific criteria[END_REF], authors proposed to use a statistical model based on acquired data from a measurement campaign. Their model includes parameters for several pathologies and patient's profiles. However, the model does not include data for intraarticular needle injection of the desired joint (knee, shoulder, wrist), which limits its use to our purpose.

In [START_REF] Yang | Modelling of needle insertion forces for surgical simulation[END_REF], it is proposed a model for needle insertion for surgical simulation. The authors proposed a first-order model that includes the pre-puncture, puncture and friction forces. They obtain the parameters from acquired data and compare the estimated force from their model with one obtained during its measurements. The force given by the model and the one capture resembles closely, but the problem with this method is the lack of consideration of several layers of tissue, as their parameters and model were calculated only works for a liver.

III. METHODOLOGY

In the case of needle insertion, it is necessary to know which forces act during the needle's injection and withdrawal, and how to compute them from the virtual environment to the haptic device. Considering that the needle shaft rubs against the tissue while the needle cuts the skin, three main forces are generated during the tissue piercing [START_REF] Kataoka | Measurement of the Tip and Friction Force Acting on a Needle during Penetration[END_REF].

1) The cutting force, which acts on the tip of the needle in the axial direction. Its magnitude differs according to the various tissue layers and the shape of the needle tip.

2) The clamping force, which acts on the side of the needle shaft in the normal direction.

3) The friction force, which acts along the side of the needle in the axial direction. In Fig. 1, are shown the involved forces during the needle injection. In addition to these forces, another force is generated when the needle is withdrawn from the tissue, and this acts on the needle's opposite motion direction; this force is called attracted force [START_REF] Barbé | Needle insertions modelling: Identifiability and limitations[END_REF] generated in the axial direction.

Bone

Skin

Fat

Subacromial Bursa

Muscles C l a m p i n g F o r c e s C u t t i n g F o r c e s F r i c t i o n

Fig. 1: Interaction forces during the needle insertion.

A. Cutting force and attracted force: Enhanced tracking wall

To reproduce the cutting force, we opted to use the Tracking Wall method (introduced in [START_REF] Alamilla | A new method to render virtual walls for haptic systems: Tracking wall. Application to needle insertion simulation[END_REF]) due to its lowresource consummation. This method is based on a virtual spring to compute the cutting forces. The difference with classic methods is that we avoid using any damper to dissipate the energy and substituted it with a second wall that ensures a constant force, and tracks the tool position during the displacement. On this occasion, the tracking wall method was improved to compute the cutting force during the needle insertion and the attracted force when it is withdrawn from the tissue. Also, it was modified to work from left to right (vice-versa) and from top to bottom (vice-versa) giving the possibility to set the virtual limb in any part of our workspace.

The tracking wall method helps us to achieve some objectives concerning the needle puncture, like the reproduction of different tissue stiffness, the ability to perform stops inside the virtual articulation, and the small rejection force generated by the muscles when the needle stops inside the joint.

As it is mentioned in [START_REF] Alamilla | A new method to render virtual walls for haptic systems: Tracking wall. Application to needle insertion simulation[END_REF], the method uses two virtual walls (Fig. 2). The first one, ω 0 is the established position of a fixed wall, when the tool collides with the skin (virtual environment). The second one, ω n , is the position of a moving virtual wall that is updated during the tool displacement or when a stop occurs. In the case the needle is moving inside the joint, ω n is updated by [START_REF] Kurita | Haptic rendering of a needle insertion by enhancing the real force response of a base object[END_REF]. This process is declared as "Update with movement" and considers if the tool enters from right to left (and vice-versa) or top to bottom (and vice-versa).

ω n = x t -∆ new if x t > ω 0 x t + ∆ new if x t < ω 0 (1)
Where x t is the current needle position, and ∆ new is a variable called "safety position", that computes the minimum distance required to reproduce the desired force, and also it represents the distance between the current point and ω n while the tool is moving.

From this, ∆ new is calculated by

∆ new = f d K t (2)
Where f d is the desired force, established by a force profile. K t is a tuning parameter that allows establishing the value of the "safety position" and reducing the value of ∆ new , which should be calculated for each layer according to the desired forces.

When a stop occurs inside the virtual limb, as ω n is behind the tool, it remains a minimum distance between the current point and ω n which we called difference distance (∆ h ) and it is calculated by

∆ h = x t -w n (3)
This distance should be reduced at each iteration to avoid having a too high rejection force when the user stops. Ideally ∆ h → 0 by the end of the last iteration, but we can also leave ∆ h with a small value, which will produce a small rejection of force that can be interpreted like the one the muscles exert on the needle. This rejection force can be set according to the properties of the involved muscles or part of the body. To update ω n when a stop occurs, we use (4), and this process is called as "Update with stop".

ω n = m * i + ω n-1 if i ≤ n ω n if i > n ( 4
)
where m is calculated by ( 5) and must remain unchanged during the rest of the process.

m = x t -ω n n (5)
Where n is the total number of iterations to update ω n , and i is the current number of iteration. Concerning the value n, this must be tuned according to the target hardware and programming language. It is suggested that the tracking wall method must be implemented in a real-time environment, so n can be set in seconds and designers can have a better manipulation of the wall update.

The purpose of this algorithm is to move the wall in front of the tool, so the next time the user moves the tool, he will face it again. To compute the cutting force (f cutting ) we use

f cutting = K t (∆ h ) (6)
As the attracted force is contrary to the cutting force, it is established that f cutting = -f attracted , so (6) can be used to calculate it by only applying the corresponding sign. The flow diagram on Fig. 3 shows the condition established to compute the forces f cutting and f attracted depending on the tool's direction. Only one of these forces is applied to the haptic system at each iteration.

Resuming it, the main advantages of the method tracking wall are:

• No biomechanical models are used. So, the method only requires low computing resources. • Ability to render the different tissues of the body, by applying several successive tracking walls or by changing the desired forces in real-time • Ability to configure the rejection force to provide realism when the needle is rejected by the tissue.

B. Clamping force: Virtual fixture

As one of our objectives is to allow the doctor to choose the entry point on the virtual joint. The trajectory and the clamping forces need to be recalculated every time that the point of insertion is changed. It is thus not possible to establish a predefined trajectory for every case.

During a real injection, the muscles constraint the movement of the needle in angular and cartesian space, making difficult to change the desired trajectory. When the needle is piercing the skin, the user can keep its initial trajectory or he can change the needle's orientation to reach the desired target. This change is limited by an angle α which allows a maximum angle of 45 • concerning the needle main axis, forming a 3D cone (Fig. 4). So, as the needle keeps going deeper and deeper α → 0, limiting the orientation until it can not be longer changed. α can be calculated by

α = tan -1 ( n L -d t n L + d t ) (7)
where n L represents the needle's length, and d t the distance computed from the point where the needle pierced the tissue until the current point To limit the tilting of the needle established by α, an orientation control can be implemented on an actuated wrist.

To ensure that the needle follows the desired path once the orientation has been set, it is necessary to create a virtual guide that limits the needle's movement in the cartesian space. If the user tries to depart from this trajectory, a correctional force is exerted to keep it in. By computing the projection point onto the line, it is possible to create a virtual fixture [START_REF] Lehmann | Analytic Geometry[END_REF]. This is made by creating a line using the insertion point of the needle (P 0 ), and the director vector ( u P ), which represents the reference trajectory and the sense of the needle. The current position of the needle is denoted by P and its projection onto the line is denoted as Q, that represents the desired point onto the line (Fig. 5). π Fig. 5

: Orthogonal projection of a point a line

To compute the projection of the point P the vector u P helps us to find an orthogonal plane π that fulfills the expression:

π P π u P ⊥π
To find Q we calculate the orthogonal projection of a point onto the line. Ideally, if the difference between P and Q is minimal, no correctional force must be exerted. On the other hand, if the positions have a significant deviation, the correctional force will be exerted with a significant value to correct the needle's trajectory.

To calculate the correctional force, we apply a proportional control which helps to correct the user movement, once the tool quits from the desired trajectory [START_REF] Podder | Needle insertion force estimation model using procedure-specific and patient-specific criteria[END_REF]. These forces represent the clamping forces.

f c = k * e ( 8
)
where e is the euclidean distance of ||QP || and k is a tuning value that grant the restriction of the needle's movement, depending on the depth and the tissue stiffness.

IV. EXPERIMENTAL RESULTS

To test experimentally that the enhanced Tracking wall and the virtual fixture can render the haptic force from a needle injection, we make tests simulating an intraarticular injection on a shoulder. For testing the forces, our test bench consists of a Virtuose™ 6D Desktop haptic device, which offers up to 10N of peak force, set in a base with a syringe mock and support for the hand (Fig. 6a). To visualize the needle injection, we implement in CHAI3D the tracking wall and the virtual fixture (Fig. 6b). CHAI3D is a framework written in C++ that give us the required libraries and functions to implement a fully 3D environment and compute collisions between the needle and the virtual articulation [START_REF] Conti | The CHAI libraries[END_REF]. This section presents some tests implementing the enhanced tracking wall method to compute the cutting and the attracted forces. The objective is to corroborate that the proposed method can render the same forces that are generated in a real procedure.

For the first test, the method is tested in 1 DoF, where the objective is to compute the desired cutting force of 1N during the needle insertion, this value is chosen from the experiment performed by [START_REF] Gordon | Needle insertion force model for haptic simulation[END_REF], and a desired attracted force of 0.5N is established empirically by our medical partner. Fig. 7 shows the results in 1 DoF. In the first instance, the tool is in free motion (FM), it means that there is no contact with the virtual articulation, and hence there is no exerted force. From t = 24s to t = 27s the needle is inserted inside the virtual joint. ω n begins to follow it, with a small ∆ h error to ensure the desired force. During this displacement, the cutting force increases until reaching the desired cutting force of 1N , while the speed remains negative. From t = 27s to 29.5s an intentional stop occurs, and when the needle's speed is almost zero, ω n updates its position until it reaches the needle's actual one. During ω n updating, the force starts to decrease until zero, to avoid a too high rejection force during the stop. When the intentional stop ends, the needle begins to be withdrawn from the virtual body from t = 29.5s to t = 31.5s. At this moment, an attracted force is computed with the chosen magnitude of 0.5N. Finally, the needle is completely withdrawn from the articulation to the free zone motion, where the user can choose another point of insertion.

The objective of the second test is to set a profile of six different cutting force values (based on the numbers of layers from an articular joint: skin, muscle, bursa, tendon, cartilage, and bone) in 3 DoF. The desired cutting forces are 1N (layer 1), 2N (layer 2), 1.5N (layer 3), 2.5N (layer 4), 3N (layer The cutting forces are set based on [START_REF] Gordon | Needle insertion force model for haptic simulation[END_REF] and by our medical partner. Fig. 8 presents the needle position and the tracking walls along each axis during the tool insertion and the tool withdrawal, these positions are computed respecting to the reference frame of the haptic device Fig. 6a. As it can be appreciated, the wall tracks the needle displacement during the tool insertion and withdrawal, keeping a difference distance (between the needle tip position and ω n ) to ensure the desired forces. In this case, as it is only tuned one K t for all the layers, it was proposed to set K t = 500N/m to reduce the difference distance as much as possible without perturbing the haptic response.

The cutting and attracted force are shown in Fig. 9. The first force increases decreases according to the stiffness of the layer (t = 0s to t = 0.75s). As it is shown, the force response is almost instantaneous when this goes from 2N to 1.5N. This is due to the fact that ∆ new is smaller when the cutting force goes from a high force to a low one. Then when the needle is withdrawn from the virtual joint, the attracted force is generated (t = 0.75s to t = 1.5s). In this case, the attracted is negative because it is opposite to the cutting force.

B. Clamping Forces

This test is performed in 3 degrees of freedom, computing the virtual fixture previously described in section III-B. Fig. 10 shows the tool displacement (blue line) and the clamping force's behavior (pink arrows) during the needle insertion inside the virtual articulation. Fig. 11 shows the forces exerted by the simulator. In this test, the user chooses the point of insertion in the virtual environment (Start point), then he continues inserting the needle until point 1, but as he does not arrive at the desired target, the tool is withdrawn, but not totally. Following the previous path of 1, the user performed a change on the needle's trajectory at 2 and finally, he reached the desired target. After achieving the objective, the user withdraw the needle from the articulation in its totality.

Fig. 10 shows that at the beginning of the test, the user almost follows the desired trajectory. Consequently, this provokes that the magnitude force is small (Fig. 11). As the user keeps moving deeper, the clamping force starts to rise due to the user departing from the desired trajectory (Fig. 11). Then the user withdraw the needle to change the path and proceed to insert it again, but during the procedure, the user keeps trying to depart from the desired path, so the clamping forces are computed to correct his movements.

As can be seen, the user can perform a correctional change of trajectory limited by the maximum α angle. When this occurs the direction of the needle changes, the haptic device exerts a force preventing the user changes his trajectory, but as the forces are not large enough to stop him, the trajectory is changed. This behavior is also similar to a real injection task when a medical student tries to re-orient the needle into the tissue.

V. CONCLUSIONS

The enhanced tracking wall allows us to compute the cutting force when the needle pierces the tissue, and the attracted forces when the needle is withdrawn from it. The benefits of this method are that we avoid the use of any complex biomechanical model, and therefore the method does not require high computing resources, allowing easy implementation on mid-range computing systems and realtime applications. The clamping forces are computed from the projection point of the current needle's position on the desired trajectory. With this method the calculated normal forces imitates the ones that the surrounding muscles exert on the needle.

As future work, the enhanced tracking wall method will be tested by using pre-calculated values of a needle injection provided by a biomechanical model developed by one of our partners. The objective of this test is to create a more realistic simulator.

Another point to improve is the reproduction of the friction forces to increase the realism of the haptic feedback.

Finally, a campaign of measurements will be carried out in order to record medical doctors with different experiences (senior, residents, and novices). Their gestures will be analyzed in order to highlight their differences and to help novices to improve their gestures.

KFig. 2 :

 2 Fig. 2: Tracking wall representation.

Fig. 3 :

 3 Fig. 3: Flow diagram to compute cutting force and attracted force.

Fig.

  Fig. Angle definition.

Fig. 6 :

 6 Fig. 6: Needle injection simulator

Fig. 7 :

 7 Fig. 7: Tracking wall -1 DoF

Fig. 8 :Fig. 9 :

 89 Fig. 8: Tracking wall -3 Dof

Fig. 10 :Fig. 11 :

 1011 Fig. 10: Virtual fixture: representation of "Clamping forces "

ACKNOWLEDGMENT

The authors would like to express their gratitude to the Consejo Nacional de Ciencia y Tecnologia (CONACyT) in Mexico for providing financial support to the PhD student and we would like to thank the ANR (French National Research Agency) for financing SAMSEI project(ANR-11-IDFI-0034) under the supervision of Pr. X. Martin.


Michel Chammas
email: michel.chammas@balamand.edu.lb

Abdallah Makhoul
email: abdallah.makhoul@univ-fcomte.fr

Jacques Demerjian
email: jacques.demerjian@ul.edu.lb

Writer identification for historical handwritten documents using a single feature extraction method

Keywords: Writer identification, historical documents, artificial intelligence, sift descriptors

With the growth of artificial intelligence techniques the problem of writer identification from historical documents has gained increased interest. It consists on knowing the identity of writers of these documents. This paper introduces our baseline system for writer identification, tested on a large dataset of latin historical manuscripts used in the ICDAR 2019 competition. The proposed system yielded the best results using Scale Invariant Feature Transform (SIFT) as a single feature extraction method, without any preprocessing stage. The system was compared against four teams who participated in the competition with different feature extraction methods: SRS-LBP, SIFT, Pathlet, Hinge, Co-Hinge, QuadHinge, Quill, TCC and oBIFs. An unsupervised learning system was implemented, where a deep Convolutional Neural Network (CNN) was trained using patches extracted from SIFT descriptors. Then the results were encoded using a multi -Vector of Locally Aggregated Descriptors (VLAD) and applied an Exemplar Support Vector Machine (E-SVM) at the end to compare the results. Our system achieved best performance using a single feature extraction method with 91.2% mean Average Precision (mAP) and 97.0% accuracy.

I. Introduction

Knowing the identity of the writer in historical manuscripts is one of the most important challenges for humanitarians [START_REF] Rehman | Writer identification using machine learning approaches: a comprehensive review[END_REF]. They depend on many specific characteristics to determine the typography and the era of the text. One of their main concerns is to identify or predict the scribe of the text, where most of the time stay uncertain. In many manuscripts, the writer's name is mentioned at the last page in a small paragraph called colophon [START_REF] Buduroh | Colophon in the hikayat pandawa manuscript[END_REF], through which they could recognize the identity of the writer. They will be able to compare the writing style of the text to other manuscripts, which belong to the same writer. This work requires a considerable amount of time and effort. Whilst the development of automated learning techniques facilitated the prediction of writer identity, but this topic remains a challenge with the difficulty of reading some texts or detecting their origins [START_REF] Asi | On writer identification for arabic historical manuscripts[END_REF]. We developed a system based on machine learning algorithms to identify writers of historical handwritten manuscripts. Our main concern was to make sure that our system generalizes well on test data. Therefore, we decided to validate our system and assess its performance on a public dataset.

Many international competitions were done on this topic: ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLAMM) [START_REF] Cloppet | Icfhr2016 competition on the classification of medieval handwritings in latin script[END_REF], ICDAR2017 CLAMM [START_REF] Cloppet | Icdar2017 competition on the classification of medieval handwritings in latin script[END_REF] and the latest was ICDAR2019 Competition on Image Retrieval for Historical Handwritten Documents (ICDAR-2019-HDRC-IR) [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. We evaluated our system on the leading and well-known competition ICDAR 2019 dataset [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. A total of four teams participated in the competition with multiple submissions of different methods. The system of the first team, from the Universitat Autònoma de Barcelona (UAB), was used as a baseline system based on the Sparse Radial Sampling of Local Binary Patterns (SRS-LBP). The second team from the South China University of Technology (SCUT) participated with two different methods submissions as follows: SIFT with Fisher vector encoding and Pathlet with VLAD encoding. The third team from the University of Groningen submitted five methods: Hinge, Co-Hinge, QuadHinge, Quill and TCC. While the fourth team from the University of Tebessa submitted only one method using oriented Basic Image Features (oBIFs).

In the proposed system, we used SIFT as feature extraction method along with CNN to train the system using patches extracted from SIFT descriptors. Then we encoded the results using multi-VLAD (with 5 layers) and applied an exemplar SVM at the end to compare the results. We used l2-norm to normalize the data at each stage. We yielded the best results with 91% mAP and 96.9% accuracy. The result of each method will be presented in the evaluation section, where we compare all the results of all mentioned methods with our system.

The rest of this paper is organized as follows: Section II presents briefly the related works in this field, Section III shows the dataset used and how the images are divided, Section IV describes the proposed system and its functionality, Section V evaluates the system results and compares them with the competition results, and Section VI concludes the paper.

II. Related works

This section briefly reviews related works to writer identification. Previous research worked initially on the techniques of extracting writer-specific features from handwritten patterns. Several methods were used for feature extraction like SIFT, RootSIFT, Contour, Hinge, Path Signature and many more handcrafted techniques [START_REF] Xiong | Text-independent writer identification using sift descriptor and contour-directional feature[END_REF] [START_REF] Lai | Offline writer identification based on the path signature feature[END_REF]. Afterward, the extracted features descriptors were either classified using traditional classifiers such as distance-based classifier, Support Vector Machines (SVM), Hidden Markov Model (HMM) and Fuzzy based classifier, or trained using a deep neural network [START_REF] Nguyen | Text-independent writer identification using convolutional neural network[END_REF]. Neural Networks were also used for automatic feature extraction [START_REF] Fiel | Writer identification and retrieval using a convolutional neural network[END_REF].

Most of the previous techniques were based on supervised learning through a single or a combination of multi handcrafted feature extractors. He. et al used handcrafted features by computing the junctions of the handwriting along with SVM. But they used clustering to create an unsupervised approach, which proved better features extraction [START_REF] He | Historical document dating using unsupervised attribute learning[END_REF]. However, deep neural network, specially CNN, showed superior performance as unsupervised learning [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF].

Fiel and Sablatnig used CNN to automatically extract the features vector. In their approach, they eliminated the last layer of the network and computed the distance using ChiSquare to identify the writers [START_REF] Fiel | Writer identification and retrieval using a convolutional neural network[END_REF].

Christlein et al. used CNN along with SIFT for feature extraction and encoded the result vector with Gaussian Mixture Models (GMM) [START_REF] Christlein | Writer identification using GMM supervectors and Exemplar-SVMs[END_REF]. According to Christlein, CNN achieved better performance when using the SIFT descriptors as a local feature extractor [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF].

Lai et al. introduced a different approach based on path signature, where they extract the pathlets from the polygonized handwriting contours. This technique requires first to binarize the image and extract the contour of the text. Then the extracted features are clustered using kmeans to create a codebook [START_REF] Lai | Offline writer identification based on the path signature feature[END_REF].

Nguyen et al. used CNN end-to-end deep-learning method to extract local features, then they aggregated the results in a global feature vector. After that, they used a softmax classifier to predict the writers without requirement of prior identification of features [START_REF] Nguyen | Text-independent writer identification using convolutional neural network[END_REF].

Based on those studies and experiments, we concluded that using SIFT in conjunction with CNN as feature extraction method would acquire the best results. We improved the approach of Christlein [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF] by training a CNN to extract local features from image patches. The SIFT was used to extract keypoint decriptors and image patches, where the descriptors were clustered and used to label the image patches. The local features extracted by the CNN network were used to create a VLAD codebook, which was encoded and normalized to perform as a global descriptor. In the next section we will describe the dataset used in our proposal.

III. Dataset

The ICDAR dataset consists of a large number of Latin historical handwritten documents. The training data contains 300 writers contributing 1 page, 100 writers contributing 3 pages, and 120 writers contributing 5 pages resulting in 1200 images of 520 writers. And the testing data contains 20,000 images, about 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages. Fig. 1 shows two samples of Manuscripts used in the dataset.

The metrics considered during the competition are the mean Average Precision (mAP) and Accuracy [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. TABLE I shows more details (origins, number of writers and number of images per writer) about the dataset used in ICDAR 2019 [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. The dataset is publicly available at the following URL: https://doi.org/10.5281/zenodo.3262372.

IV. System description

In order to perform writer identification for historical handwritten manuscripts, two steps are required: feature extraction and classification.

A. Features extraction

The unsupervised Scale Invariant Feature Transform (SIFT) [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF] method is used to localize potential image samples for feature extraction. SIFT method has the advantage of being invariant to basic transformations such as scaling, rotation and translation. Centered at each keypoint, a 32x32 patch is extracted from the document image. Each patch is mapped to a 128 dimensional SIFT descriptor vector. The importance of a SIFT descriptor is that it is invariant to transformation (scaling, rotation and translation). Fig. 2 shows a sample of the extracted SIFT keypoints and their descriptors.

In order to improve the recognition performance and obtain a compact representation, the Principal Component Analysis (PCA) method (with whitening) [START_REF] Chen | Semisupervised feature learning for improving writer identification[END_REF] is applied on the SIFT descriptors in order to reduce their dimensionality. We reduce the descriptors dimension from 128 to 32. This reduction helps to decrease the processing cost of the descriptor clustering operation.

B. Clustering

We used the mini batch k-means clustering technique [START_REF] Christlein | Writer identification using GMM supervectors and Exemplar-SVMs[END_REF] (with initial size of 15000) to group the descriptors into 5000 clusters. The clusters are used as labels for image patches. We randomly extracted 990k patches with their respective labels (cluster centroids) from the training data (275 patch per document). These patches were used to train a deep Convolutional Neural Network (CNN) to be used as a feature extractor [START_REF] Nguyen | Text-independent writer identification using convolutional neural network[END_REF]. The CNN we used is a Residual Neural Network with 20 layers. To train the network, we used 900k patches for training and 90k for testing.

C. CNN training

We trained a CNN to map image patches into their corresponding labels. The CNN is then used as a feature extractor [START_REF] Nguyen | Text-independent writer identification using convolutional neural network[END_REF]. We split the data for training and validation, where 90k are used for validation. We implemented a deep residual network with 20 layers (ResNet20), where local descriptors were extracted with CNN for each document [START_REF] Fiel | Writer identification and retrieval using a convolutional neural network[END_REF]. Fig. 3 shows the accuracy rating of the ResNet20 training. After extracting the CNN features, we created a UBM from all the descriptors in the training data and clustered them using k-means to 100 clusters. Then we applied l2-norm for data normalisation [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF].

l2(v) = ||v||2
(1)

||v||2 = a1 2 + a2 2 + a3 2 (2)

D. Encoding

A VLAD codebook, acting as a Universal Background Model [START_REF] Rehman | Writer identification using machine learning approaches: a comprehensive review[END_REF], is formed via k-means clustering of all the local descriptors in the training data into a set of 100 clusters. The VLAD codebook is then used to encode each document [START_REF] Christlein | Encoding CNN activations for writer recognition[END_REF]. For instance, local descriptors of each document are Then we applied l2 norm to normalize the VLAD vectors [START_REF] Christlein | Writer identification and verification using GMM supervectors[END_REF].

To further improve performance, we used the multi-VLAD approach in which we created 5 different codebooks and concatenated their global encodings into a 32000 dimensional global descriptor. Finally, we reduce the global descriptor dimensionality to 3200 via regularized PCA [START_REF] Christlein | Encoding CNN activations for writer recognition[END_REF].

Σ = 1 n -1 (X -x) T (X -x) (3)
x = 1 n n i=1
x i (4)

E. Similarity

At the end, we compared the documents by computing the cosine distance between their VLAD vectors. Than we implemented Exemplar-SVM (ESVM) using a linear kernel [START_REF] Malisiewicz | Ensemble of exemplar-SVMs for object detection and beyond[END_REF] [START_REF] Christlein | Unsupervised feature learning for writer identification and writer retrieval[END_REF], as a result we reached an improved precision and accuracy.

V. Evaluation

The system of the first team from UAB was used as a baseline system based on the method of Nicolaou et al. [START_REF] Nicolaou | Sparse radial sampling LBP for writer identification[END_REF], the Sparse Radial Sampling of Local Binary Patterns (SRS-LBP) and Hellinger Kernel, they achieved 86.6% mAP and 93.1% accuracy for the retrieval using Manhattan to compute the distance [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. The second team from the South China University of Technology (SCUT) participated with three submissions, one with two combined features (SIFT and Pathlet) and two with a single feature extraction method as follows: SIFT with Fisher vector encoding and Pathlet with VLAD encoding. On the first stage before feature extraction, they performed two pre-processing steps. The first step is a text binarization using deep Unet, while the second step is a page-level rotation Correction to align the text. In their Fig. 3: ResNet20 training and validation accuracy first submission, they extracted SIFT features and used the Fisher Vector method to encode them. They achieved their best result with 90.6% mAP and 96.6% accuracy. In their second submission, they extracted Pathlet features and encoded them with VLAD. They got 89.8% mAP and 96% accuracy [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. The third team from the University of Groningen also applied binarization as a pre-processing step and used X2 distance to compute the scores. They submitted five methods: Hinge (75.6% mAP and 88.4% accuracy), Co-Hinge (84.5% mAP and 92.9% accuracy), QuadHinge (80.2% mAP and 91.3% accuracy), Quill (76% mAP and 88.3% accuracy) and TCC (79% mAP and 89.7% accuracy) [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. The fourth team from the University of Tebessa submitted only one method using oriented Basic Image Features (oBIFs) without any preprocessing and used Euclidian distance to calculate the result: 84.6% mAP and 92.7% accuracy [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]. TABLE II shows the results of all submissions (teams, methods and scores).

In contrast to the other systems, our system works directly on raw images where no preprocessing step is involved and uses a neural network-based approach. We used a single method: for feature extraction (SIFT) and trained a CNN using the SIFT descriptors. We encoded the results using VLAD and computed the similarity scores using cosine distance (89.7% mAP and 95.5% accuracy). Then we tried to compute the distance using ESVM (linear kernel) and we succeeded to improve the results by around 1.5%. We got the best result compared to similar submission of the SCUTT team using SIFT with 91.2% mAP and 97.0% accuracy. The importance of our system is that we achieved a better result to the same method without any preprocessing step. We applied SIFT, which was implemented by Christlein et al. in 2017 [START_REF] Christlein | Writer identification using GMM supervectors and Exemplar-SVMs[END_REF], to extract the features directly from the images provided in the dataset. While the SCUTT team first used a deep Unet for text binarization, which can achieve a high precision recall and improve the accuracy performance by 1-2% [START_REF] Li | Deepunet: A deep fully convolutional network for pixel-level sealand segmentation[END_REF]. Second, they performed a page-level rotation correction step based on the line projection method in order to make the text more horizontal. They used the extracted binary and gray-scale images to perform the feature extraction in their both methods. The SCUTT team won the ICDAR 2019 competition as they achieved the highest mAP with two combined methods SIFT and Pathlet [START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF], but we were able to yield the better mAP and accuracy with our system using a single method.

VI. Conclusion

In this paper, we presented a performant system for writer identification using only one feature extraction method and without any pre-processing techniques. Our system presents best results compared to other participants. It is notable that SIFT is the best method for feature extraction as it surpassed all the other methods. Also, we showed that ESVM improved our results when used as a replacement of the cosine distance to compute the distance between the VLAD vectors. For future work, we will take into consideration the use of two feature extraction methods. Also, since all the competitions were challenged with Latin Handwritten Manuscripts and we did not find any assessed Historical Arabic Handwritten dataset, we will test our system on the historical Arabic manuscripts

Fig. 1 :

 1 Fig. 1: The image on the left represents a sample page of the training data and the image on the right represents a sample page of the testing data

TABLE I :

 I Image providers and number of writers used in the test dataset[START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF]

	Provider	City	nb writers nb images/writer images total
	Manuscripts		2027		10135
	Bodleian Libr.	Oxford	9	5
	BVMM		586	5
		Boulogne	28
		Chantilly	30
		Nantes	13
		Rennes	16
		Saint-Omer	363
		Toulouse	12
		10 writers p. repository	124
	Cambridge Dig. Libr.	2	5
	e-codices	Geneva	2	5
	Gallica		1352	5
		Amiens	14
		Paris	1232
		Reims	41
		Valenciennes	52
		8 writers p. repository	13
	Harvard		19	5
	Stanford	Baltimore (Walters)	57	5
	Letters A		831		2655
	Univ. Library	Erlangen
	-Erlangen-Nurnberg		290	1
			170	3
			371	5
	Letters B		2052		2052
	Univ. Library Basel	Basel	2052	1	2052
	Charters		5158		5158
	Monasterium		5158	1	5158
	Total		10068		20000
	aggregated into a 6400 dimensional global descriptor vector.

TABLE II :

 II The results of ICDAR 2019 competition with single methods compared to our result[START_REF] Christlein | ICDAR 2019 competition on image retrieval for historical handwritten documents[END_REF] that are available at the Digital Humanities Center at the University of Balamand. The collection contains more than 500 Arabic manuscripts from the 8th to the 19th century. It includes more than 11000 images for more than 500 scribes, and gathered from more than 10 locations between Lebanon and Syria. At the end, we look forward for the next competition.

		Method	Accuracy [%] mAP [%]
	Baseline	SRS-LBP (a) Classification	92.2	84.0
		SRS-LBP (b) Retrieval	93.1	86.8
	SCUTT	SIFT	96.6	90.6
		Pathlet	96.0	89.8
	Groningen	Hinge	88.4	75.6
		Co-Hinge	92.9	84.5
		QuadHinge	91.3	80.2
		Quill	88.3	76.0
		TCC	89.7	79.0
	Tebessa	oBIFs	92.7	84.6
	Our System	SIFT	97.0	91.2

Acknowledgement

This work has been supported by the EIPHI Graduate School (contract ANR-17-EURE-0002).