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Antoine Italiano

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Madame Juge

Michèle KIND……………………………………..…………….……….. Juge Monsieur Le Le Docteur

Jean-Michel Coindre……………………………………………… Professeur

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Rapporteur Professeur

Nathalie Lassau

Optimal combination of conventional MRI and DCE-MRI parameters to early predict pathologic response to anthracycline-based neoadjuvant chemotherapy for locally advanced high-grade soft-tissue sarcomas DIRECTRICE DE THESE

Keywords: MRI, magnetic resonance imaging OR, Odds Ratio PE, Peak enhancement PHR, poor histological responder ROC, receiver operator characteristics ROI, region of interest SI, signal intensity STS, soft tissue sarcoma Ve, extravascular interstitial volume plasmatic volume WI, weighted imaging WIn, Wash in

Purpose : To determine which conventional MRI and Dynamic-contrast-enhanced MRI (DCE-MRI) qualitative, semi-quantitative, quantitative features are significantly associated with histologic response to anthracycline-based neoadjuvant chemotherapy (a-NAC) in locally advanced high grade soft-tissue sarcoma (STS). To estimate inter-and intra-observer agreements of each criterion.

Methods:

Retrospective, monocentric exploratory study. 46 adults with biopsy-proven high-grade STS of trunk walls and extremities, uniformly treated in a French sarcoma reference centre with 5-6 cycles of a-NAC. Radiological evaluation consisted in conventional pre-therapeutic (t 0 ) MRI 0 and after 2-3 cycles of a-NAC (t 1 ) MRI 1 +DCE-MRI 1 . After 5-6 cycles of a-NAC (t 2 ), 32 patients also had MRI 2 +DCE-MRI 2 for pre-operative evaluation. All had available pathological analysis of surgical specimen with treatment response estimation. Good or poor histologic response (GHR, PHR) was determined depending on % residual viable tumour cells (10% or >10%, respectively). MRI were randomly analysed, two times by two radiologists, blinded to histological results.

Conventional MRI lecture assessed tumour maximal size, bi-dimensional diameters, volume, as well as T1 and T2 signals, architecture, margins, oedema, nerve, vessels and bone invasion in a dichotomised fashion. DCE-MRI was analysed on whole tumour surface (ROI s ) and on the first area displaying contrast uptake (ROI N ): qualitatively (%tumour volume enhanced during arterial phases, curvology), semiquantitatively (model-free: wash-in (WIn), iAUC90, Peak-enhancement (PE), and their ratio versus healthy muscle) and quantitatively (extended Tofts model: K trans and its ratio).

Se, Sp, PPV, OR were determined, as well as best cut-offs helped by receiver operating characteristics (ROC) curves analyses. Significant criteria to distinguish GHR and PHR at t 1 were used in a logistic binary regression to build a multivariate model to predict response. Inter-and intra-observer agreements were estimated.

At t 2 , correlation between %viable cells and numeric DCE-MRI 2 indices were evaluated with uniand multivariate linear regression.

Results: 34 PHR and 12 GHR were included. Gender female was significantly associated with PHR (p=0.015). After 2-3 cycles of a-NAC, none of the change in measurements or on conventional MRI was significantly different between PHR and GHR. PE and WIn-ratio on ROI N were significantly higher in PHR (p= 0.029 and 0.048, respectively). When gender, PE and WIn-ratios variables were considered together, they significantly predicted histologic response (p=0.004), but none individually reached significance. 94.1% PHR were well-classified. 45.5% GHR were ill-classified. AREA under ROC curve of the model was 0.846 IC 95% =[0.677-0.994]. ABBREVIATIONS 18-FDG-PET/CT: 18 F fluorodeoxyglucose positron emission tomography a-NAC: anthracycline based neo adjuvant chemotherapy DCE-MRI: diffusion contrast enhanced MRI DWI: diffusion weighted imaging FNCLCC: Féderation nationale des centres de lute contre le cancer GHR: Good Histological responder iAUC90: area under the curve at 90s

At t 2 : %change of longest dimension, bi-dimensional diameter, volume since t 0 were significantly higher in GHR (p=0.012, 0.010, 0.031, respectively). WIn-ratio, PE, iAUC90, K trans, , K trans -ratio in ROI N (p= 0.012, 0.010, 0.031, 0.022, 0.023, 0.020, 0,009, 0,010, respectively) as well as K tran , K trans -ratio on ROI s were lower in GHR (p=0.043 and 0.021, respectively) . Multivariate linear regression analysis showed that WIn-ratio-ROI n,2 and % change of the longest dimension were significantly correlated with % viable cells on following surgery (r=0.370, p=0.022 and r=0.394, p=0.029, respectively). t 2 analysis identified late responders profile in 4/5 ill-classified true GHR. -DCE-MRI metrics on pre-operative imaging correlated well with % of viable tumour cells on surgical specimen.

Conclusion

-Inter and intra-observers agreements of relevant DCE-MRI metrics were at least acceptable.

-Multiparametric MRI follow-up enabled to detect 'late responders' profile in about 1/3 of good responders. DISCIPLINE: Thèse de medicine. Diplôme d'étude specialisé en radiodiagnostic et imagerie médicale

INTRODUCTION

Soft tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumours that can affect any anatomical site, especially extremities and trunk wall.

At a locally stage, the recommended treatment is a wide excision with negative margins (R0) carried out by a surgeon specifically trained in the treatment of STS. Despite optimum local treatment, 35% of patients will develop metastasis and die of their cancer. Two phase III studies have demonstrated potential benefits in overall survival in locally advanced high-grade STS patients treated with anthracycline-based neoadjuvant chemotherapy (NAC), redefining the standard of care of this disease [START_REF] Issels | European Organisation for Research and Treatment of Cancer Soft Tissue and Bone Sarcoma Group (EORTC-STBSG); European Society for Hyperthermic Oncology (ESHO). Neo-adjuvant chemotherapy alone or with regional hyperthermia for localised high-risk soft-tissue sarcoma: a randomised phase 3 multicentre study[END_REF][START_REF] Saponara | Neo)adjuvant treatment in localised soft tissue sarcoma: The unsolved affair[END_REF][START_REF] Pasquali | Neoadjuvant treatment: a novel standard?[END_REF][START_REF] Gronchi | Histotype-tailored neoadjuvant chemotherapy versus standard chemotherapy in patients with high-risk soft-tissue sarcomas (ISG-STS 1001): an international, open-label, randomised, controlled, phase 3, multicentre trial[END_REF] Despite controversies and lack of validation studies, the efficacy of NAC can be estimated by the histologic response performed on the surgical specimen. It consists in the estimation of % identifiable viable tumour cells, % fibrosis or granulation tissue, % of necrosis on the whole tumour volume (Figure 1.A). Despite controversies and lack of validation studies, by analogy with osteosarcomas, the cut-off to distinguish poor from good histologic responses (PHR and GHR, respectively) is often defined at 10% (Bielack et al., 2002;Previsor et al., 1997, Lucas et al.;2008;Walderman et al., 2011;[START_REF] Andreou | Prognostic relevance of the mitotic count and the amount of viable tumour after neoadjuvant chemotherapy for primary, localised, high-grade soft tissue sarcoma[END_REF]. Recent communication has demonstrated the relevance in multivariate prognosis analysis to predict overall survival [START_REF] Cousin | Clinical, radiological and genetic features, associated with the histopathologic response to neoadjuvant chemotherapy (NAC) and outcomes in locally advanced soft tissue sarcoma (STS) patients (STS)[END_REF].

Histologic response can only be performed at the end of NAC. Biopsy under treatment would only estimate the response on a small sample of a heterogeneous lesion, in an unethically invasive manner. Thus, there is a need for STS-specific non-invasive biomarkers in order to monitor the response on the entire tumour volume [START_REF] Sullivan | Metrology standards for quantitative imaging biomarkers[END_REF]FDA-NIH Biomarker working group, 2016).

The most widely used and validated method to assess response is based on variation of tumour maximal length, the basis of the Response Evaluation Criteria in Solid Tumours (RECIST 1.1), whose advantages are reproducibility and ease to measure [START_REF] Eisenhauer | New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)[END_REF], bi-dimensional (WHO) and volume being classically not used for STS, although their value has not been estimated.

Yet, evaluations based on measures do not take into account vascular and architectural alterations that occur before size variation. The poor correlation between RECIST 1.1 and histological and clinical outcomes has been reported, notably in STS [START_REF] Canter | Radiographic and histologic response to neoadjuvant radiotherapy in patients with soft tissue sarcoma[END_REF][START_REF] Grange | The significance of size change of soft tissue sarcoma during preoperative radiotherapy[END_REF][START_REF] Messiou | European Organization for Research and Treatment of Cancer -Soft Tissue and Bone Sarcoma Group; European Organization for Research and Treatment of Cancer -Imaging Group. Evaluation of response after pre-operative radiotherapy in soft tissue sarcomas; the European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC-STBSG) and Imaging Group recommendations for radiological examination and reporting with an emphasis on magnetic resonance imaging[END_REF] or in the setting of targeted therapies for gastrointestinal stromal tumours, for instance [START_REF] Benjamin | We should desist using RECIST, at least in GIST[END_REF][START_REF] Choi | Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria[END_REF]. The RECIST working groups continuously investigating adaptation of RECIST, dealing with opposed issues such as innovation, generalization, specificity, feasibility or reproducibility, but none of the recent guidelines include adaptations for STS (Seymour et al., 2017;Litiere et al., 2017;Litiere et al., 2016). Magnetic resonance imaging (MRI) with contrast agent (CA) intravenous injection can virtually capture fibrosis and necrosis that occurred under NAC (Figure 1.B). However, the early identification of tissues that are getting fibrotic or necrotic remains challenging. These tissues can exhibit continuous alterations from normal tissue to definitive fibrosis or necrosis. In addition, MRI also enables the analysis of tumour margins, surrounding tissue and local bone, nerve and vessels invasions.

Previous studies have already demonstrated improved prediction of prognosis with modified CHOI compared with RECIST [START_REF] Stacchiotti | High-grade soft-tissue sarcomas: tumor response assessment--pilot study to assess the correlation between radiologic and pathologic response by using RECIST and Choi criteria[END_REF][START_REF] Stacchiotti | Tumor response assessment by modified Choi criteria in localized high-risk soft tissue sarcoma treated with chemotherapy[END_REF]. Through a quantification of the enhancing part of the tumour, mCHOI aims at estimating the residual viable component and to identify responders without decrease in size. Nonetheless, by being acquired several minutes after Gadolinium chelates injection, mCHOI measurements confound enhancement of the residual viable component resistant to NAC, of the fibrosis, and of components with underlying necrotic or fibrotic processes.

Dynamic-contrast-enhanced MRI (DCE-MRI) is a well-known MR technique that has been notably developed in oncology to non-invasively study blood supply and abnormal vascularization of tumours. Its applications include local staging, NAC response monitoring, and identification of residual or recurrent tumour after surgery [START_REF] Jackson | Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging[END_REF][START_REF] Padhani | Multiparametric imaging of tumor response to therapy[END_REF].

According to the Quantitative Imaging Biomarkers Alliance (QIBA) DCE-MRI technical committee, DCE-MRI consists in serial acquisitions of a 3D gradient echo volumetric T1-wi sequence at a short temporal resolution (<10s) prior (for baseline) and after paramagnetic CA IV injection in order to follow its distribution through pathological tumour vessels and through tumour interstitial space.

Four levels of DCE-MRI analysis exist (Figure 2): ..space, IVS, and extravascular extracellular interstitial space, EES) both supposed to be at diffusion equilibrium [START_REF] Tofts | Estimating kinetic parameters from dynamic contrast-enhanced T(1)weighted MRI of a diffusable tracer: standardized quantities and symbols[END_REF]. The model generates two transfer constants: K trans (volume transfer constant between IVS to EES) and K ep (efflux rate constant, between EES to IVS) and estimates fraction volumes corresponding to EES (v e ) and IVS (v p ).

(iv) The most recent level is texture analysis, which herein would aim at quantifying the vascularization heterogeneity within tumour. The underlying idea of texture analysis is to study all the information that was lost by working on average values of a ROI. Semi-quantitative and quantitative DCE-MRI provide parametric maps for each indices on which can be performed firstorder (based on histogram), second order (based on co-occurrence matrices, which can grab the heterogeneity in spatial organization of voxels) or higher order statistics. Among the multiple numeric indices derived from these complex analyses and helped by machine learning approach and classification tasks, some of them, or their combination, could be identified as surrogate biomarkers for prognosis, sensitivity to local (radiotherapy) or systemic (chemotherapy, immunotherapy, targeted therapy) treatments, and histologic response prediction, for instance [START_REF] Limkin | Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[END_REF][START_REF] Gillies | Radiomics: Images Are More than Pictures, They Are Data[END_REF][START_REF] Kumar | Radiomics: the process and the challenges[END_REF]Lamblin et al., 2012).

Identification of surrogate biomarkers among DCE-MRI parameters has been extensively studied, especially in solid tumour treated with anti-angiogenic drugs in pre-clinical and prospective trials for prognosis or response under treatment. Conclusions of clinical studies were halftone and did not validate surrogate biomarker, usable in clinical trials or daily practice [START_REF] Hahn | Dynamic contrast-enhanced magnetic resonance imaging pharmacodynamic biomarker study of sorafenib in metastatic renal carcinoma[END_REF][START_REF] Morgan | Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies[END_REF][START_REF] Bahri | Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?[END_REF][START_REF] Dingemans | First-line erlotinib and bevacizumab in patients with locally advanced and/or metastatic non-small-cell lung cancer: a phase II study including molecular imaging[END_REF][START_REF] Liu | Dynamic contrast-enhanced magnetic resonance imaging as a pharmacodynamic measure of response after acute dosing of AG-013736, an oral angiogenesis inhibitor, in patients with advanced solid tumors: results from a phase I study[END_REF].

For mesenchymal tumours, bone sarcomas (osteosarcoma and Ewing sarcoma) were among the first ones to illustrate the potential of DCE-MRI as predictors of histologic response and survival, and to help plan surgery [START_REF] Hanna | Assessment of osteosarcoma response to preoperative chemotherapy using dynamic FLASH gadolinium-DTPA-enhanced magnetic resonance mapping[END_REF][START_REF] Reddick | Dynamic contrast-enhanced MR imaging evaluation of osteosarcoma response to neoadjuvant chemotherapy[END_REF][START_REF] Dyke | Osteogenic and Ewing sarcomas: estimation of necrotic fraction during induction chemotherapy with dynamic contrast-enhanced MR imaging[END_REF][START_REF] Egmont-Petersen | Detection of areas with viable remnant tumor in postchemotherapy patients with Ewing's sarcoma by dynamic contrast-enhanced MRI using pharmacokinetic modeling[END_REF][START_REF] Uhl | Osteosarcoma: preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion-and perfusion-weighted magnetic resonance imaging[END_REF] DCE-MRI for STS has demonstrated its added value in order to predict malignancy when its qualitative analysis was associated with static post-contrast MRI in a prospective study [START_REF] Van Rijswijk | Soft-tissue tumors: value of static and dynamic gadopentetate dimeglumine-enhanced MR imaging in prediction of malignancy[END_REF], and in order to detect local recurrence [START_REF] Grande | Detection of soft-tissue sarcoma recurrence: added value of functional MR imaging techniques at 3.0 T[END_REF]. Nevertheless, this MR method has not been as used as in other solid tumours for treatment response evaluation, because of STS rarity, heterogeneity in term of histotypes, genetics and vascularization.

However, assessment of tumour response to neoadjuvant treatment for STS has demonstrated promising results despite small, retrospective or pre-clinical series with lack of homogeneity in patient population and in definition of tumour response. Actually, some considered a good response depending on % of necrosis other to 0 %, 5%, 50% viable tumour cells [START_REF] Meyer | Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates[END_REF][START_REF] Huang | Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging[END_REF][START_REF] Soldatos | Multiparametric Assessment of Treatment Response in High-Grade Soft-Tissue Sarcomas with Anatomic and Functional MR Imaging Sequences[END_REF][START_REF] Monsky | Semi-automated volumetric quantification of tumor necrosis in soft tissue sarcoma using contrast-enhanced MRI[END_REF]. Additionally, none of the neoadjuvant treatments (isolated limb perfusion (ILP), chemo-radiation, or non-mentioned) of which the effects were assessed, represented the currently recommended NAC, that is to say anthracycline-based chemotherapy.

Nonetheless, Soldatos et al. demonstrated in 23 patients the interest of qualitative assessment of arterial phase enhancement with a cut-off of 5% of early-enhanced tumour volume, without mentioning when pre-operative evaluation was performed and which NAC was used [START_REF] Soldatos | Multiparametric Assessment of Treatment Response in High-Grade Soft-Tissue Sarcomas with Anatomic and Functional MR Imaging Sequences[END_REF]. Van Rijswijk depicted the pattern of enhancement as helpful to determine the fibrotic, necrotic or tumour nature of residual lesion after ILP (Van Rijswik et al., 2003). Recently, Huang et al. demonstrated that Ktrans was of better value to predict response than %change in longest diameter at 2 weeks. But the studied population was small (N=20), included different neoadjuvant treatments (chemoradiotherapy or sorafenib+chemoradiotherapy) and they did not compare their model of quantitative DCE-MRI with the extended Tofts model recommended by RSNA QIBA alliance [START_REF] Huang | Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging[END_REF].

In pre-clinical studies with experimental sarcoma mouse model, Alic et al. showed that K trans was significantly reduced in ILP-treated mice, with a decrease of heterogeneity on cumulative histogram of K trans maps, in tumour itself and in tumour periphery [START_REF] Alic | Regional heterogeneity changes in DCE-MRI as response to isolated limb perfusion in experimental soft-tissue sarcomas[END_REF].

Viglianti demonstrated predictive value on overall survival and metastasis free survival of semiquantitative and quantitative DCE-MRI performed prior and 24h after neoadjuvant hyperthermia/radiation therapy [START_REF] Viglianti | Dynamic contrast-enhanced magnetic resonance imaging as a predictor of clinical outcome in canine spontaneous soft tissue sarcomas treated with thermoradiotherapy[END_REF].

To conclude on what has been done until now about DCE-MRI and STS under neoadjuvant treatment, clinical and experimental sets of arguments guide towards potential improvement of response assessment compared with simple measures (such as RECIST1.1). However, several points require to be fixed prior to build prospective studies with decision-making based on DCE-MRI: (i) lack of treatment homogeneity, (ii) lack of definition of optimal moment of evaluation, (iii) lack of validation of most relevant DCE-MRI (iv) lack of association with other MRI modalities, at least morphometrics analysis (v) lack of uniformly accepted definition of histological response, (vi) lack of reproducibility analysis.

Consequently, in an attempt to retrospectively address these issues, the purpose of our study was to perform a large analysis of qualitative, semi-quantitative, quantitative DCE-MRI-derived criteria associated with morphometrics MRI features of tumour itself and tumour periphery, undergoing same recommended a-NAC, at early and similar moment from its beginning, by two independent radiologists. First aim was to determine which conventional MRI and DCE-MRI features on early evaluation could predict histologic response to a-NAC in locally advanced STS patients.

*

MATERIALS AND METHODS

Our institutional review board approved this retrospective monocentric study and informed consent was waived for each patient.

Study population and design (Figure 3)

In total, 46 patients were included in the study between May 2012 and December 2016. They all met the following criteria:

-Newly diagnosed STS of the trunk wall or extremities, with pathological confirmation of the diagnosis in a sarcoma reference centre -Locally advanced, non-metastatic (assessed at least on chest computed tomography), high grade STS, according to FNCLCC [START_REF] Guillou | Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma[END_REF] -Validation of a-NAC during the regional interdisciplinary panel of the sarcoma reference centre with a total of 5-6 cycles.

-Available pre-therapeutic MRI 0

-MRI 1 and DCE-MRI 1 for early evaluation of a-NAC efficacy performed on the same MR-system at 2 to 3 cycles with DCE-MRI and conventional sequences.

-Curative surgery after achievement of the chemotherapy -Pathologic response assessment on the whole surgical specimen with estimation of % necrosis, % of fibrosis, % of viable tumour cells.

Additionally, all the patients benefited from adjuvant radiotherapy and were followed-up at our institution.

For each patient, the following data were reported: age, gender, ASA score, performance status, tumour location (shoulder girdle, pelvic girdle, upper limb, lower limb, trunk wall), pain at diagnosis, modality of diagnosis (imaging-guided micro-biopsy, surgical biopsy, incomplete surgery performed out of a reference centre and without diagnosis), histotype, delay between MRI 2 and surgery, delay between last cure of a-NAC and surgery.

Pathologic examination

Hematoxylin and Eosin-stained slides of the initial diagnostic biopsy and of the surgical specimen were all reviewed by two expert pathologists from our sarcoma reference centre, following published EORTC guidelines [START_REF] Wardelmann | Evaluation of response after neoadjuvant treatment in soft tissue sarcomas; the European Organization for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC-STBSG) recommendations for pathological examination and reporting[END_REF]. The percentage of histologically intact viable tumour (% residual viable tumour cells), necrotic tissue (%necrosis) and granulation tissue (%fibrosis) were scored per slide, and the mean weighted percentage for each component was calculated. One can note that %necrosis + % fibrosis + %viable tumour cell = 100%. Patients were classified as GHR if 10% of residual tumour cells were seen, and as PHR if >10%, by analogy to the Salzer-Kuntschik grading system for osteosarcoma [START_REF] Salzer-Kuntschik | Morphological grades of regression in osteosarcoma after polychemotherapystudy COSS 80[END_REF]. Margins status was assessed as being: R0 (macroscopically complete with negative microscopic margins), R1 (macroscopically complete with positive microscopic margins), R2 (macroscopically incomplete), after consensus between surgeon and pathologists.

MRI protocol and post-processing

All patients had an initial pre-therapeutic MRI (t0: MRI 0 ), following 2-3 cycles of a-NAC (t1 At our centre, MR imaging examination were performed on the same 1.5-T MR imaging system (AERA 1.5T Siemens Medical Solutions, Erlangen, Germany) with adapted coils depending on the tumour location. For trunk wall and girdles, a 18-channels phased-array body coil was used. For extremities, a knee 15 channels coil (knee or wrist tumour) or head 32 channels coil (ankle or foot tumour) were used.

Conventional MRI

At our centre, MRI were acquired during routine clinical practice. At least one T2-wi (turbo spin echo imaging (2D-TSE), repetition time (TR)=3020ms/echo time (TE)=136ms, thickness (th)=4mm), one T1-wi (2D-TSE, TR/TE=557/12ms, th=4mm), one orthogonal fluid-sensitive T2-wi with fat suppression (2D-TSE, TR/TE=4150/69ms, th=4mm) and one T1-wi after fat suppression and gadolinium chelates injection (3D-Dixon EDG TR/TE=6.8/2.3ms, flip angle= 10°, isotropic resolution 1mm, with multiplanar reformation), following DCE-MRI) had to be available. MRI performed in other centre had to include at least two orthogonal plans in order to measure the maximal length for RECIST 1.1, the two diameters for bi-dimensional and the volume assessments.

DCE MRI sequences acquisition and post-processing

Acquisition. Before the DCE-MRI acquisition, data for constructing a T1 map were acquired with a spoiled 3D gradient echo T1-wi sequences (TR/TE=1.82/4.65, th=1mm, intersection gap: 10-20%

(depending tumour size), FOV=240mm, matrix= with 2° and 15° flip angles)

The same volume acquisition was used for DCE-MRI sequence, which consisted in a 3D fast spoiled gradient echo sequence with following acquisition parameters: TR/TE = 4.3/1.7ms, flip angle =20°, FoV and Matrix adjusted for in plane resolution of 1,1x1,1mm, th=4mm. The volume acquisition was repeated every 8-9 seconds. At least two baseline volume acquisitions were performed prior to contrast agent injection, followed by 18 volumes after. The whole DCE-MRI lasted about 3mn30s. 0.1mM/kg of Gadoteric acid (Guerbet, France) or Gadobenate Dimeglumine (Bracco, Italy) were injected intravenously at a rate of 2mL/sec followed by a 20 mL flush of 0.9% f NaCl solution helped by a MR compatible automatic injector (Sonic Shot 7, Nemoto Kyorindo, Japan)

Post-processing. DCE-MRI were post-processed with dedicated OleaSphere software (Olea Medical, version 3, La Ciotat, France) by the same radiologist (Rad1). T1-mapping was performed with Relaxometry module to generate R0-R1-T1-T0-maps that were used to optimize DCE-MRI consecutive analysis. Depending on the contrast agent injection, signal intensity was converted in Gadolinium concentration (Gd).

Motion artefacts were corrected with a rigid-body coregistration method. The arterial input function was semi-automatically determined helped by cluster analysis technique with manual adjustment if required after control quality [START_REF] Mouridsen | Automatic selection of arterial input function using cluster analysis[END_REF]. Permeability module was used to generate semi-quantitative (Wash-In, Peak-Enhancement, AUC at 90 s) and quantitative (K trans ) parametric maps.

Wash-In (WIn, mmol.L -1 .s -1 ) corresponds to the velocity (or mean slope) of the increasing part of the curve, that is to say to the initial and mean gradient of the upsweep of the enhancement curve, calculated with linear least square method.

Peak-enhancement (PE, %) corresponds to the % of increase of the initial up-slope of the curve during the acquisition time.

iAUC90 is the area under the time-vs-[Gadolinium] curve following the 90s after detection of contrast agent detection. It represents a mix between blood volume and permeability.

We decided not to generate parameters related to return transfer of contrast agent from EES to IVS (Wash-out, K ep ) because our acquisition was limited to 3mn30 in order to focus on the early phases of the enhancement and because our protocol was integrated in the daily practice with time constraint and risk of motion artefact.

Control quality. Global quality of the arterial input function and parametric maps were estimated according to a 3 points-scale (0: non-diagnosis DCE-MRI, 1: poor quality non-usable for quantification, 2: good). Miscalculations on K trans map, defined as discordance between tumour enhancement on raw data and K trans map, were reported and excluded for quantitative analysis.

Reader procedure

Two radiologists (Ra1 and Ra2, with 2 and 25 years of experience in musculoskeletal soft-tissue tumours MRI, respectively), blinded to the imaging and histopathology reports, independently evaluated the MRI studies. Ra1 performed two lectures with a delay of 2 months between the 2 sessions. The order of MRI reading was randomly modified for the second session to limit memory bias. For imaging criteria based on qualitative assessment of conventional MRI, a final consensual reading between Ra1 and Ra2 was made in case of interpretation disagreement.

4.1.Conventional MRI analysis

Location. Tumour was qualified as deep, deep + superficial and superficial, depending on its situation relative to the superficial aponeurosis.

Measurements. For each MRI (t 0 , t 1 , t 2 ), radiologists determined the maximal length of the tumour (L1), the two diameters ('a x b') on the maximal surface on axial sequence. Additionally, Ra1

manually segmented all the tumours on T2-wi sequence, slice-by-slice, with the help of other sequences for more accurate delineation, in order to calculate tumour volume (Vol).

Tumour Architecture. Each radiologist estimated the percentage of necrosis, fibrosis and haemorrhage on a three points scale (0: 10%, 1: 10-50%, 2: 50%). Necrosis was defined as component with very low signal intensity (SI) on T1-wi, fluid-like very high SI on T2-wi, with irregular borders and without enhancement after CA injection. Fibrosis was defined as component with very low SI on T1-wi and T2-wi, below muscle SI, with possible late enhancement.

Haemorrhage was defined as a component with high SI on T1-wi, remaining high on T1-wi with fat suppression.

Tumour Periphery and local extension. Radiologists reported the presence or absence (1 or 0) of ( 1)

ill-defined margins (2) peripheral oedema on T2-wi with fat suppression, (3) enhancement of the oedema after CA injection and (4) vascular, nervous and/or bony invasion.

T2-ratio. Relative T2-ratio (= SI tumor /SI muscle ) is a numeric parameter that was introduced to assess response in rectal cancer with decrease of this ratio being interpreted as a reflection of fibrosis [START_REF] Kluza | T2 weighted signal intensity evolution may predict pathological complete response after treatment for rectal cancer[END_REF]. T2-ratio = SI tumor /SI muscle where a region of interest (ROI) is drawn on a representative surface of the tumour to obtain SI tumor , and a second ROI is carefully drawn on a healthy muscle of other muscular lodge, avoiding intramuscular fat to obtain SI muscle .

Variations under treatment (01, 02).

RECIST 0  1 , RECIST 0  2 , WHO 0  1 , WHO 0  2 , Vol 0  1 ,
Vol 0  2 were calculated for each patient. Apparition of sero-hematic levels within the tumour was reported. Variations of tumour architecture and tumour periphery variables were qualified as 0:

similar, 1: decreased, 2: increased.

Different types of changes could be assessed: mainly fibrotic, mainly necrotic, mixed fibro-necrotic and no evolution. Among those with fibrotic changes, we distinguished a pattern consisting in large fibrosis of the tumour, while a nodular tissue component was absolutely unaffected on conventional sequence (Figure 4) with a tendency to grow, suggesting resistance to a-NAC. We called this 'compartmental fibrosis' (to differentiate it from peripheral fibrosis, diffuse internal fibrosis, internal septa fibrosis, no fibrosis).

Finally, on T1-wi and T2-wi, radiologists were asked to estimate on a 3 point-scale the variation of heterogeneity (0: less heterogeneous, 1: similar, 2: more heterogeneous)

4.2.DCE-MRI analysis (Figure 5)

ROI positioning. The two radiologists had to manually place three ROI that were propagated on the set of parametric maps (WIn, PE, iAUC90, K trans ). Conventional MRI and DCE-MRI were all available to help ROI placement. One ROI was placed on the maximal surface of the whole tumour (ROI S ), one ROI of at least 1cm 2 was placed on the first component of the tumour that displayed CA uptake on raw DCE-MRI data (ROI N , supposed to represent its most aggressive component)

and one ROI on healthy muscle from another muscular lodge (reference ROI M ) Qualitative analysis. On raw DCE-MRI data, radiologists identified the sequences that were acquired prior to venous filling, on which they estimated the percentage of arterial enhancement of the tumour (0: 10%, 1: >10%).

After ROI placement, they qualified the morphology of the time-vs-Gd curve for ROI S and ROI N according to a two points-scale (1: slope and/or plateau  3 x slope and/or plateau of the healthy muscle ROI 3 , 0: below). In case of disagreement between the readers during the consensual reading, the most unfavourable option (1) was taken.

Semi-quantitative and quantitative analyses. Indices value for each ROI was reported in order to calculate the following parameters:

Absolute value: X-ROI i , j

Ratio relative to the healthy muscle: X-ratio-ROI i , j = X-ROI i , j / X-ROI M , j

Where i  s,n, corresponding to the two tumour ROIs, j  0,1,2, corresponding to the time of evaluation, and X  Ktrans, WIn, PE, iAUC90. Thus, a total of 4 x 4 = 16 parameters were available for each MRI, as well as their change, exposed as: %Change-X 0  j = 100 x (X-ROI i , j -X-ROI i , 0 ) / X-ROI i , 0 %Change-X-ratio 0  j = 100 x (X-ratio-ROI i , j -X-ratio-ROI i , 0 ) / X-ratio-ROI i , 0

Statistical analysis

-Normality was assessed for each continuous value by using Shapiro-Wilk test. Depending on the results, the student t-test or the Mann-Whitney U-test was performed to compare good and poor histologic responders.

-Categorical and ordinal parameters were compared using the Chi-2 or Fisher test -For significant association between continuous parameters and histologic response, receiveroperating curves were built in order to quantify the performance of each individual parameter and to find the optimal cut-off points that satisfy the Youden index (i.e. point farthest from chance, to minimize rate of misclassification) [START_REF] Perkins | The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve[END_REF]. Sensibility (Se), specificity (Sp), odd-ratios (OR) with 95% confidence interval (CI 95% ) for these cut-off points were calculated. Thus these continuous variables were dichotomised (0: under the cut-off, 1: above).

-The relevant clinical variables, conventional MRI 1 variables, DCE-MRI 1 variables, all dichotomised as dummy variable, were analysed in multivariate analysis by using a binary logistic regression, with entry method, to estimate multivariate OR with their IC. -Correlation between the imaging parameters that were significantly associated with histologic response on late MRI 2 , and the percentage of viable tumour cells, were estimated using univariate linear regression (with Spearman's rank correlation or Pearson test depending on normality test result), followed by multivariate linear regression.

-The inter-and intra-observers reliabilities were assessed using interclass correlation coefficient (ICC) for continuous values (with two-way mixed and one-way random models respectively). The Cohen's Kappa () test for pairwise agreement was used for dichotomous variable. Weighted kappa ( w ) statistics was used for ordinal variable (herein RECIST 1.1 and OMS at early (01) and late (02) evaluations).

The agreement for ICC was defined as good (>0.75), moderate (0.5-0.75) or poor (<0.5). The agreement for classical  and  w was defined as slight (0-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80) and almost perfect (0.81-0.99) [START_REF] Landis | The measurement of observer agreement for categorical data[END_REF].

- Median age was 58 years (range, 33 -77) Median time between baseline MRI 0 and the first cycle of treatment administration was 28 days (range, 0 -94).

Median time between last cycle of a-NAC and surgery was 29 days (range, 19 -81).

Median time between MRI 2 and surgery was 25 days (range, 1-50).

34/46 (73.9%) were classified as PHR and 12/46 (26.1%) as GHR after pathological analysis on surgical specimen.

Table 2 summarizes the salient initial clinical and histological features.

Among the 18 included females, only 1/18 (5.5%) was GHR, while 17/28 (60.7%) males were GHR (p=0.015, OR univariate = 11 (1.42 -124.60))

Conventional MRI data (Table 3)

Initial features. No significant difference was found between GHR and PHR at baseline, whatever the location, the architectural features, and the peripheral features. t0t2. From MRI 0 to MRI 2 , there was a significant difference between PHR and GHR for the fibrotic pattern (p=0.041).

T2-ratio, T2-SD-ratio at t 0 , t 1 and t 2 , and their %change from t 0 to t 1 and t 0 to t 2 did not show significant difference.

DCE-MRI data

A total of 101 DCE-MRI were acquired (23 DCE-MRI 0 , 46 DCE-MRI 1 and 32 DCE-MRI 2 ). 7 were considered as non-diagnostic for any type of DCE-MRI analysis. Among the 94 remaining DCE-MRI, 12 displayed mismatches between raw data and K trans map, that is to say, highly and early perfused tumour with K trans values equal 0. Thus, a total of 82 DCE-MRIs were used for K transrelated analyses.

Qualitative analysis. Analysis of the curve morphology on ROI s or ROI n did not reveal significant difference between GHR and PHR at t 1 . However, at t 2 , 15/24 (62.5%) PHR showed a type 3+4 curves for the ROI n on versus none of the GHR (0/8, 0%) (p=0.003).

Estimation of less than 10% of tumour volume with arterial phase enhancement did not differ between GHR and PHR at t 1 : 4/34 (11.8%) PHR and 3/12 (25%) GHR displayed this feature. At t 2 , this became significantly different: (3/24 (12.5%) PHR versus 6/8 (75%) GHR, p=0.002).

Semi-quantitative and quantitative analyses.

Inter and intra observer agreements of the relevant criteria at t 1 and t 2

Table 5 shows the agreements for each relevant criterion among measures, conventional MRI and DCE-MRI studies. None of the conventional MRI criteria showed both inter-and intra-reader at least substantial agreement. At t 1 and t 2 , PE-ROI n,1 and WIn-ratio-ROI n,1 demonstrated moderate to good inter-and intra-reader agreements. At t 2 , among the Ktrans-related parameters, only K transratio-ROI n and K trans -ratio-ROI s showed both satisfying inter and intra-reader agreements.

Combination of significant criteria at t 1 to predict response

Due to their poor reproducibility's, none of the conventional MRI criterion were introduced in the multivariate analysis. After dichotomisation of PE-ROI n,1 and WIn-ratio-ROI n,1 according to the thresholds that were found on ROC analysis and verification of the absence of collinearity between them (r 2 =0.147), a logistic binary regression was conducted. The model included gender, PE-ROI n,1

and WIn-ratio-ROI n,1 . When the 3 predictors were considered together, they significantly predicted histologic response (p=0.004). The model enabled to explain 38.7% of the variation of the outcome (Nagelkerke-R 2 =0.387), fit well with the data (Hosmer-Lemeshow test, p=0.340), with a diagnostic accuracy of 84.4%. Indeed, only 2/34 (5.9%) PHR were ill classified as good responders. Thus, Se was 94.1%, Sp 54.5%, PPV 86.5% and AUC of ROC curve of the model was 0.836 IC95% (0.677 -0.994) to detect PHR.

However 5/11 (45.5%) GHR were ill classified as poor responders. None of the 3 predictors alone significantly predicted the histologic response when all were introduced in the model. Table 6 shows the results of the binary logistic regression. Figure 8 shows ROC curve of the model. Finally, after each predictor being dichotomised, the equation of the model was:

Particularities of myxoid-round cells liposarcomas response with a-NAC

Our series included 6 myxoid-round cell liposarcomas (M/RC-LPS), which were all considered as PHR after pathological analysis of surgical specimen. In 4 of 6 cases, the pathologists specified that the round cell component had disappeared and that only remained the myxoid component raising the question of how to evaluate pathologic response in M/RC-LPS.

Retrospective analysis of the M/RC-LPS showed a particular imaging pattern of response. Maximal diameter, minimal diameter and volume were not modified between t 0 and t 1 (paired Wilcoxon, p>0.05). Conventional MRI 0 showed well-limited tumour, without peripheral oedema. At t 1 , 2/6

showed small haemorrhage on MRI 1 . Fibrosis <50% was reported in 4/6 patients with apparition of either septa, diffuse or peripheral fibrosis. Fatty component also increased suggesting a REdifferentiation with a-NAC.

DCE-MRI 1 thresholds were poorly efficient. PE-ROI n,1 was positive in one case. WIn-ratio-ROI n,1 was positive in 4 cases. Thus, one male patient had all the three model predictors negative leading to misdiagnosis. In this case, one must note that round cells component was absent on surgical specimen. Figure 9 shows a typical evolution of M-RC LPS treated with a-NAC.

Retrospective analysis of ill-classified responses to highlight clues to avoid misclassification

(Figure 10).

In order to identify the limits of the multivariate model, we retrospectively analysed the images of the 5 ill-classified true GHR and the 2 ill-classified PHR.

Four of the 5 true GHR did not demonstrate any suggestive sign of a response to treatment at t 1 :

neither on conventional MRI 1 , nor on DCE-MRI 1. However, these 4 true GHR displayed morphological and perfusion alterations between t 1 and t 2 strongly evocative of a good response: 3/4 turned into entire fibro-necrotic tumours, without arterial phase enhancement, 1/4 remained morphologically poorly unchanged. However, this patient benefited from a DCE-MRI after 4 cycles of a-NAC showing less than 10% of arterial phase enhancement, WIn-ratio-ROI s and PE-ROI s under cut-offs. These four cases suggest the possibility of 'late responders to a-NAC' (Figure 11.A).

One of the 5 true GHR still demonstrated at t 2 one component with perfusion abnormalities ('Aggressive' time-vs-Gd curve, WIn-ratio-ROI s,2 = 5.3 mmol.L -1 .s -1 ) (Figure 11.B).

One of the 2 ill classified true PHR was a M/RC-LPS (see section 7. of results for details). CA injection and at arterial, venous and late phases), shows patchy venous and late enhancements, with disappearing of some enhanced area on DCE-MRI 2. (C) Curves at early and late evaluation: at t 1 , ROI Ncorresponding curve (red) was classified type-1, that is to say 'aggressive' compared with muscle curve (blue). At t 2 , the curve was classified type-0. Altogether, DCE-MRI data were compatible with a late good response, but tumour was pathologically classified as PHR because of >10% viable cells despite no round cells. 9. Multiparametric MRI analysis of a-NAC resistant compartment (Figure 11)

Conventional MRI analysis at t 1 enabled to distinguish 12 patients with a heterogeneous response, called 'compartmental fibrosis' because one component clearly exhibited a strong fibrotic response, while at least one another component was absolutely not affected by a-NAC (persistent nodule).

One was a GHR and 11/12 were PHR.

Among the 12 cases of compartmental fibrosis at t 1 :

-2/12 patients had MRI 0 + DCE-MRI 0 without any clues for a future persistent nodule (Figure 12.A).

-9/12 patients only had MRI 0 without any clues on conventional analysis for a future persistent nodule.

-1/12 patient had a tumour of which MRI 0 + DCE-MI 0 demonstrated a different nodule that would grow under a-NAC while the rest of the tumour got fibrotic (Figure 12.B). Interestingly, this component was moderately vascularized compared with the others at t 0 .

Correlation between relevant DCE-MRI parameters at t 2 and percentage of viable cell on surgical specimen (Figure 12, table 7)

We believed that one explanation of the failure of our model to correctly classify 45.5% of good responders was due to the lack of criterion to estimate good response, that is to say, to estimate %fibrosis and %necrosis. %Fibrosis estimated on MRI 1 was not reproducible enough to be acceptable in a model.

On another point of view, we hypothesized the ability of the multivariate model to correctly classify 94.1% of poor responders was likely due to the positive relationship between DCE-MRI numeric indices and residual viable cells.

To test this hypothesis, we performed univariate and multivariate linear regressions that took into account the significant criteria to distinguish PHR and GHR at t 2, prior to surgery. In this part of the study, we also made the assumption that modifications of tumour between t 2 and surgery would remain limited, thus conventional MRI 2 and DCE-MRI 2 would be direct reflection of the tumour.

The other aim of this part is to capture the information contained in continuous data (% of viable cell) and lost with dichotomisation (PHR or GHR with a cut-off of 10% of residual viable cells).

Table 8 and figure 13 summarizes the results of univariate and multivariate linear regression between %viable tumour cells on surgical specimen and quantitative possible numeric predictors, beforehand identified as significantly different between GHR and PHR at t 2 . Finally, multivariate analysis demonstrated that WIn-ratio-ROI n,2 and %change of the longest dimension showed significant correlation with % viable tumour cells(r=0.370, p=0.022 and r=0.394, p=0.029, respectively). Together with results at t 1 , this highlights the potential of WIn-ratio-ROI n as a biomarker of pathological response. Their combination significantly predict % of viable tumour cells (ANOVA, F=6.249 ; p=0.002) Using these 3 predictors simultaneously, adjusted R 2 was 0.337, meaning that 33.7% of variance of % viable tumour cell could be predicted by their combination Qualitative criteria, even significantly different between GHR and PHR at t 2 , were removed from the analysis because of lack of reproducibility, as well as % Change-'axb' 02.

Gender and PE-ROIn,2 were not significantly correlated with %viable tumour cell and were consequently not included in the model. iAUC90-ratio-ROI n,2 was also removed because of collinearity with other criteria (variance inflation factor = 3.625, tolerance = 0,276). * : P<0.05 ; ** : p<0.01, *** :p<0.001. All cases with missing data were removed from the analysis.

11. Identification of different peripheral oedema: inflammatory response versus tumour oedema (Figure 13).

We hypothesized that different types of oedema could occur during the NAC. Indeed, at initial evaluation, peripheral oedema is often associated with enhancement after CA injection especially around the tumour, very likely to represent tumour cells spreading and infiltrating surrounding tissue. We called it 'tumoral oedema'.

During the course of a-NAC, we observed a qualitatively different oedema pattern, with a more linear enhancement and a decrease of the extension of pure oedema. This process was seen around fibrotic processes. We called it 'peri-granulation inflammation' and we hypothesized that it consisted in inflammatory cells infiltrate.

The features 'presence of an oedema', 'increase of fibrosis t 0 t 1 ' and 'peripheral enhancement' did not demonstrate significant difference between GHR and PHR at early evaluation t 1 . In order to inventory patients with 'peri-granulation inflammation', we combined these three features at t 1 and selected patient with oedema and peripheral enhancement actually occurring around fibrotic process. At t 1 , this combination of features was present in 6 patients, 5/6 GHR and 1/6 PHR. Measures variations are the easiest features to obtain but demonstrate important limits. They occur after angiogenesis and architectural modifications. Size can increase secondary to necrosis, haemorrhage, tumour primary or secondary resistance. Size can decrease due to favourable event such as necrosis or retractile fibrosis, or because one compartment displays excellent response while another is clearly not affected by NAC.

Architectural features on DCE-MRI, despite subjectivity, highlight patterns of response: (i) mainly fibrotic, (ii) mainly necrotic, (iii) fibro-necrotic, and (iv) no apparent response. Interestingly subpattern of fibrosis could be discriminated: (i) diffuse, (ii) septa, (iii) peripheral and (iv) compartmental fibrosis. The 'compartmental fibrosis' sub-pattern stresses the hypothesis of primary or secondary a-NAC resistant component within a tumour that mainly favourably responds.

However, there is a strong assumption that architectural 'macroscopic' MRI features follow modifications of tumour vascularization, assessed by DCE-MRI.

Improvement of early prediction with DCE-MRI

After 2 cycles of a-NAC, semi-quantitative DCE-MRI indices were able to significantly predict a poor histologic response with an excellent sensitivity (94.1%), while variations of longest diameter, bi-dimensional diameters, volume and conventional MRI qualitative analysis and T2-ratio were not significantly different between PHR and GHR, except for estimate of %fibrosis, of which inter and intra-observer reproducibilities were low.

At the end of a-NAC, all the measures, the pattern of fibrosis, several items of qualitative, semiquantitative and quantitative analyses of DCE-MRI predicted histologic response.

Overall, these results support the idea of a continuum of perfusion, architectural and dimensional alterations.

We believed that integrating the whole information from each modality of evaluation, instead of being exclusive, would improve response prediction.

However, the model (with or without taking into account gender) failed to identify 41.7% (5/12, one of them having poor quality DCE-MRI 1 which was removed from the semi-quantitative and quantitative analyses) true GHR at early evaluation. MRI follow-up of one of these 5 patients demonstrated a 'compartmental fibrosis': one compartment remaining unchanged, questioning in this sole case, the coherence between radiological and pathological results. The other cases demonstrated features compatible with GHR on late evaluation, highlighting a 'late responders' phenotype on MRI (about 33.3% of all GHR).

Occurrence of compartmental fibrosis made us considers the hypothesis of mechanisms of resistance to a-NAC either before (primary resistance) or after the beginning of chemotherapy (secondary resistance).

We wondered if retrospective analysis of MRI 0 and DCE-MRI 0 could have helped to identify imaging features of potential primary resistance and, if yes, which DCE-MRI features would be associated. Only 3 of the 12 patients with compartmental fibrosis underwent both MRI 0 and DCE-MRI 0 . Two of them did not demonstrate any evidence of different imaging features at t 0 . One tumour initially demonstrated a less vascularized area at the right place of the future persistent nodule. From this observation, one could hypothesize that resistance would came from combination of hypoxia (eventually assessed by PET-CT or PET-MRI or Spectroscopy-MRI) and lower provided dose of chemotherapy Furthermore, difficulties to detect these resistant components -if truly pre-existing (that is, preexisting different molecular features) -could also be due to limitations of our MR sequences and analysis methods. Another hypothesis could also be that the nodule secondarily appeared (apparition of molecular heterogeneity after and/or due to a-NACsecondary resistance).

Further multi-parametric MRI studies could explore in more details these two patterns (compartmental fibrosis and late responders) in order to detect them at initial or early evaluation with better accuracy and to target them for imaging guided biopsy (MRI or fusion Ultrasound-MRI). Sampling and making tumour molecular screening of these sub-groups of STS could potentially identify primary or secondary resistance pathways (and help adjust treatment toward neo-adjuvant radiotherapy or ILP) to NAC, as well as underlying molecular particularities of late responders.

Genomics, transcriptomics, proteomics knowledges and applications in oncology have incredibly raised and, additionally to radiomics analogical approach, imaging must participate to this infatuation by looking for every deviance to expected results.

Correlations between % viable tumour cells and DCE-MRI indices

In an attempt to evaluate the information lost by dichotomising variables, we demonstrated that % of viable tumour cells was positively correlated with several DCE-MRI metrics, notably, in the multivariate analysis, with WIn-ratio-ROI t . All the variations of measures also demonstrated significant correlations. If confirmed, these results could help treatment decision such as surgical planning, additional other treatment (radiotherapy, ILP)

4. M/RC-LPS: how to evaluate response to treatment?

Both radiological and pathological analyses were limited to estimate response in these cases. All were classified as PHR. However, in 4 cases, pathologists noticed they did not find round cells among the numerous residual tumour cells. MRI and DCE-MRI did not report tumour shrinkage or necrosis, as it has been described with Trabectedin [START_REF] Grosso | Efficacy of trabectedin (ecteinascidin-743) in advanced pretreated myxoid liposarcomas: a retrospective study[END_REF][START_REF] Hollebecque | Inadequacy of size-based response criteria to assess the efficacy of trabectedin among metastatic sarcoma patients[END_REF].

On the contrary, tumours tended to remain stable at early evaluation. Later, at t 2 , we found size decrease with apparition of fatty component and some haemorrhagic foci as well as DCE-MRI parameters decrease. These responses suggested a re-differentiation pattern, similar to the 'non dimensional tumour response' pattern with Trabectedin, proposed by Sanfilippo and Casali (San Filippo et Casali, 2013). In their review, the authors distinguished this pattern from 'tumour shrinkage' pattern. They explained its occurrence by a specific activity of Trabectedin that would target and block FUS-CHOP transcript leading to adipogenic differentiation. Thus, our observations suggest that this activity may also be possible with anthracycline-based NAC.

Work in progress

 Conventional MRI and DCE-MRI comparisons of imaging response of local cases (Bergonie institute) of M/RC-LPS under neoadjuvant Trabectedin (n=6) and Anthracycline-based chemotherapy (n=9) with available final pathological analysis and surgical specimen samples.

We will investigate the similarity of: (i) the 'non dimensional tumour response' and (ii) targeting of FUS-CHOP transcript with a-NAC Nonetheless, assessment of percentage of round cells (%RC) at diagnosis is challenging. >5 %RC is associated with poorer outcome, but is quantified on a small micro-biopsy that only estimated a very small amount of the tumour [START_REF] Fiore | Myxoid/round cell and pleomorphic liposarcomas: prognostic factors and survival in a series of patients treated at a single institution[END_REF][START_REF] Haniball | Prognostic factors and metastatic patterns in primary myxoid/round-cell liposarcoma[END_REF]. Recent studies have tried to address this issue [START_REF] Gimber | MRI characteristics associated with high-grade myxoid liposarcoma[END_REF] but results are biased because the gold standard was %RC on surgical specimen AFTER neoadjuvant treatment and MRI was performed prior.

Thus, conventional, functional, texture MRI features of M/RC-LPS associated with poor prognosis (and supposed to reflect initial %RC) remain mainly unknown.

Prospects

 There is a need for collaborative retrospective multicentric study to investigate the prognostic value of MRI radiomics-based indices enriched with bioclinial data that would predict disease free survival and overall survival of M/RC-LPS.

For those patients who underwent direct curative surgery without neoadjuvant treatment, correlations between conventional MRI, DCE-MRI, MRI-radiomics metrics and %RC should be assessed.

Patterns of peripheral oedema and immune reactions during a-NAC

STS surrounding oedema, prior to any treatment, is a well-known phenomenon. It is said to correspond to reactive zone, beyond tumour periphery or pseudocapsule, made of neovascularization, oedema and satellite tumour cells, partly responsible for tumour local recurrence [START_REF] Enneking | The effect of the Anatomic setting on the results of surgical procedures for soft parts sarcoma of the thigh[END_REF][START_REF] White | Histologic assessment of peritumoral edema in soft tissue sarcoma[END_REF][START_REF] Hanna | Muscle edema in musculoskeletal tumors: MR imaging characteristics and clinical significance[END_REF].

Looking at pre, per and post-therapeutic MRIs, we identified another original pattern of surrounding oedema evolution, which we called 'peri-granulation inflammation'. We defined it as a persistent but decreased surrounding area of high SI on T2-wi, T2-FS-wi or DP-FS-wi associated with illlimited peripheral enhancement adjacent to a fibrotic process. In case of peripheral fibrosis pattern, this enhancement was located on each side of the fibrotic coating (displaying low SI on T1-wi, T2wi), giving a 'black-rim' aspect. This pattern was seen in 6/46 patients: 1 PHR and 5 GHR.

This 'black rim' looked different from the classical pseudocapsule, due to compression and atrophy of peripheral tissue, because it was initially absent and was observed in superficial tissue where no muscle tissue could have been pushed and compressed.

We hypothesize that this oedema may reflect a favourable immune inflammation leading to fibrosis of the tumour. Further studies to investigate its precise nature, its association with histological or molecular features, and its prognostic impact are required.

Work in progress

 At t 2 , we also looked for the cases also demonstrating 'peri-granulation inflammation' and 'tumoral edema'.

 We are going to analyse the HES slides that sampled the tumour margins on surgical specimen to look for which type of cellular infiltrate was surrounding the tumour, either inflammatory cells (and which subtype of cells) or tumour cells  If histology actually confirms different cells infiltrate, we will investigate the tumour surrounding tissue with multiparametric imagings to better characterize its features.

Limits

Our study has several limits: 6.1. This is a retrospective study, but this step is mandatory before planning prospective study in oncology.

6.2. This is a single-centre study, but one aim of our work is to obtain an uniformly-treated population. Antracycline-based NAC is becoming the reference NAC, performing better than histotype-tailored NAC but few centres actually prescribed it alone, without radiotherapy [START_REF] Gronchi | Histotype-tailored neoadjuvant chemotherapy versus standard chemotherapy in patients with high-risk soft-tissue sarcomas (ISG-STS 1001): an international, open-label, randomised, controlled, phase 3, multicentre trial[END_REF].

6.3. Furthermore, our patient population (N=46) could appear small but, to the best of our knowledge, this is the largest effective to evaluate MRI +DCE-MRI values to predict histologic response.

6.4. One could argue that our patient population is made of different histotypes. We pooled together different high-grade STS due to their rarities, their inclusion in same clinical trials, leading to same treatment, same follow-up in same sarcoma reference.

Alveolar soft part sarcomas, known to be hyper vascularized, were naturally excluded because the reference treatment consists in anti-angiogenic therapy [START_REF] Reichardt | Chemotherapy in alveolar soft part sarcomas. What do we know?[END_REF]Kummar et al., 2013) Larger studies would have to perform multivariate analysis taking into account histotypes, notably, M/RC-LPS.

6.5. The treatment response was evaluated in this study using % viable tumour cell on pathological analysis of surgical specimen by analogy with osteoasarcomas [START_REF] Salzer-Kuntschik | Morphological grades of regression in osteosarcoma after polychemotherapystudy COSS 80[END_REF].

Other centres use % of necrosis or mitotic count [START_REF] Andreou | Prognostic relevance of the mitotic count and the amount of viable tumour after neoadjuvant chemotherapy for primary, localised, high-grade soft tissue sarcoma[END_REF] as a markers of response.

Recent ASCO communication demonstrated that this criterion with this cut-off was also predictive of overall survival [START_REF] Cousin | Clinical, radiological and genetic features, associated with the histopathologic response to neoadjuvant chemotherapy (NAC) and outcomes in locally advanced soft tissue sarcoma (STS) patients (STS)[END_REF]. A consensual definition appears crucial.

The method of quantification, namely a percentage of tumour volume without taking in account the absolute tumour volume, could be questionable. We observed cases where component that were compatible with viable tumour on MRI, strongly decreased. Yet, these cases were considered as PHR because the necrosis got cleaned between t 1 and t 2 , and because fibrosis tended to retract.

Response of massively necrotic tumours is also difficult to estimate for both radiologists and pathologists. On a radiological point of view, the positioning of ROI N on the first enhanced component on DCE-MRI sequence is tricky or even unfeasable, some tumours only exhibiting a thin peripheral CA uptake. On a pathologist point of view, liquefied necrosis and cystic compartment are often cored, evacuated during the fresh state step and excluded from the final percentages, leading to over-estimation of % fibrosis and % viable cells, a case that we encountered in this study. A solution could be to combine MRI estimation of necrosis and pathological analysis.

Monsky et al. proposed a semi-automated MRI method to segmentate necrosis with good correlation [START_REF] Monsky | Semi-automated volumetric quantification of tumor necrosis in soft tissue sarcoma using contrast-enhanced MRI[END_REF].

Finally, a 5µm slice is used to sample a volume of 1cm3, chosen because it seems to be representative at the macroscopy step, relying on subjectivity of the pathologist and of the technician.

6.6. Our method to analyse conventional MRI, qualitative DCE-MRI and to place ROI S , ROI N and ROI 3 for the semi-quantitative and quantitative analyses also relies on subjectivity. Nonetheless, when used by specialized radiologists from a sarcoma reference centre, DCE-MRI inter-and intra-observers agreements were acceptable, except for model based indices, and in the range of %change of longest diameter, on which is based on RECIST1.1. On a practical point of view, working on average values from manually placed ROI represents the easiest and less time-expensive way to obtain information. We believed than in the setting of future clinical trials that would include DCE-MRI in imaging evaluation, this method could be of interest.

6.7. We decided to evaluate ratio of the indices in addition to their absolute values because tumour location, depth, arterial input function, MRI coils greatly differed in our population study. The % of tumour volume with enhancement at arterial phase has demonstrated ability to predict response in prior studies, reason why we aimed at specifically studying its DCE-MRI properties in our ROIbased analysis [START_REF] Soldatos | Multiparametric Assessment of Treatment Response in High-Grade Soft-Tissue Sarcomas with Anatomic and Functional MR Imaging Sequences[END_REF]Van Rijswik et al., 2003).

6.8. We decided not to analyse DCE-MRI parameters related to the late part of the acquisitions, that is wash out and return transfer from tissue to plasma, because our DCE-MRI protocol only last 3mn30s.

6.8. DCE-MRI is a complex method that required trained team of technicians and radiologists to respect the technical recommendations of RSNA QIBA alliance. Nonetheless, we had to remove (18.8%) 19/101 quantitative DCE-MRI from the study: 7 because of poor quality (movements, inadequate arterial input function) and 12 because of mismatch between raw-data, semi-quantitative metrics and K trans (Figure 14). One reason could be inadequacy of Tofts-Kety model to estimate STS vascularization, especially for distant and strongly arterialized location, but also to accumulations of factors contributing to biases (temporal downsampling, too short total scan time AIF and T1-mapping of poor quality, low signal-to-noise ratio, B1 inhomogeneity, partial volume effect, errors in haematocrit, distance between tumour and arterial input, injection rate…) [START_REF] Lavini | Simulating the effect of input errors on the accuracy of Tofts' pharmacokinetic model parameters[END_REF][START_REF] Rukat | Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties[END_REF]. (B) However, the raw data, the curve and all the other maps were coherent with an entirely hypervascularized tumour (100% of tumour volume enhanced at the first volume acquisition following CA injection). We believe that miscalculation was due to the combined effect of massively hyper-vascularized effect at distant, under-sampling (temporal resolution = 8-9s) and poorly perfused distal location.

6.9. Another limitation is the lack of initial DCE-MRI 0 acquisition for each patient. After control quality, % changes of DCE-MRI indices from t 0 to t 1 were estimable in 21 patients for semiquantitative analysis (5 GHR and 16 PHR), and 17 patients for quantitative analysis (4 GHR and 13 PHR). Therefore, no conclusion could be drawn from differences between GHR and PHR at t 0 and from t 0 to t 1 . Difference at t 1 may probably be present at pre therapeutic evaluation 6.10. At t 2 evaluation, we found that DCE-MRI 2 indices correlated well with % viable cells suggesting that DCE-MRI would reflect the viable cells component. However, DCE-MRI indices would probably not help to provide a % viable cells at any instant because evolution of % viable cells as a function of days since beginning of NAC probably does not follow a linear function. Here, we did not look for estimators of fibro-necrotic processes but directly to estimators of viable cells, because classification as PHR and GHR relies on their percentage.

Thus, in addition to the proposal of late responders, the lack of estimators of fibrosis and necrosis could explain the limits of our strongly perfectible model. Nonetheless, even imperfect, our goal to demonstrate the potentiality of DCE-MRI indices has been reached.

Prospects

Validation of the semi-quantitative DCE-MRI 1 criteria on external cohort

The thresholds of the two DCE-MRI 1 indices need to be validated before being included in clinical trials design. DCE-MRI lacks of test-retest reproducibility, remains technically challenging, and is very sensitive to conversion of SI to gadolinium chelates concentration. To do so, we are currently assessing their diagnostic performance on a retrospective cohort, with images acquisition made on our previous 1.5-T MR-system. We are also prospectively assessing it on a prospective cohort on our current MR-system through an ancillary study of the Neosarcomics clinical trial (NCT02789384)

Work in progress

 Retrospective validation: Data are currently being analysed (N= 25 with same inclusion criteria). Acquisition were performed on our previous 1.5T MR-system between 2007 and2012.

Future work

 Prospective validation will be performed once Neosarcomics trial inclusion will end and once pathological analysis on all surgical specimens will be available

Potential therapeutic consequences of current results

Our results show that a significant proportion of GHR could be mistaken as PHR with RECIST1.1, OMS, volume variation, MRI and DCE-MRI at early evaluation, as well as the existence of a late responders profile on imaging. Current knowledge is not strong and reliable enough to stop neoadjuvant treatment after early evaluation, but rather to perform an intermediate evaluation after

4 cycles. Identification of potential PHR at t 1 could also lead to MRI-guided biopsy in order to sample the residual tumour and screen potential targetable molecular pathways. In case of late responders, this screening could help to identify different pathways of response to a-NAC.

Moving beyond pathological analysis: towards a radio-pathological evaluation of response?

First, to overcome the under and over estimation of necrosis, fibrosis and residual viable cells, we believe that the post-operative assessment should be consensually made by radiologists and pathologists.

Prognosis impact of multi-parametric MRI should be assessed, alone, and integrated with the whole potential other predictors. Herein, with the oldest case being surgically treated at the end of 2012, the latest at the beginning of 2017, we do not have the necessary hindsight to estimate the prognostic value of each MRI and DCE-MRI indices of the tumour and its periphery.

Technical optimisation for DCE-MRI

Arterial spin labelling (ASL) is MRI sequence of which the aim is to perform perfusion MRI with endogenous tracers instead of intravenously injected exogenous CA and does not rely on mathematical conversion of SI. It consists in the labelling of arterial spins with a radiofrequency pulse associated with a magnetic field gradient before they arrive in the acquisition volume and secondarily spread within its vessels and tissue. To our knowledge, ASL has not been used to study soft tissue tumours but recent publications has demonstrated its feasibility for musculoskeletal application [START_REF] Partovi | Advanced Noncontrast MR Imaging in Musculoskeletal Radiology[END_REF].

Adding other 'metrics' to improve prediction

Other functional quantitative imaging modalities could bring additional values: 18-FDG PET CT scan [START_REF] Benz | Combined assessment of metabolic and volumetric changes for assessment of tumor response in patients with soft-tissue sarcomas[END_REF][START_REF] Benz | FDG-PET/CT imaging predicts histopathologic treatment responses after the initial cylce of neoadjuvant chemotherapy in high-grade soft tissue sarcomas[END_REF]Hermann et al., 2012;[START_REF] Evilevitch | Reduction of glucose metabolic activity is more accurate than change in size at predicting histopathologic response to neoadjuvant therapy in high-grade soft-tissue sarcomas[END_REF] and spectroscopy MRI [START_REF] Zhang | Synergistic role of simultaneous PET/MRI-MRS in soft tissue sarcoma metabolism imaging[END_REF], elasto-MRI, diffusion-weighted images [START_REF] Soldatos | Multiparametric Assessment of Treatment Response in High-Grade Soft-Tissue Sarcomas with Anatomic and Functional MR Imaging Sequences[END_REF]. In addition, instead of classical ROI-averaging analyses that loose the information about tumour heterogeneity, texture and shape analyses, both parts of 'radiomics', could identify new numeric biomarkers. Radiomics refers to the extraction of several quantitative indices, which may not perceptible by radiologist's eyes, within digital images from any imaging modality. Texture metrics estimate heterogeneity, derived from histogram, co-occurrence matrices, fractal analysis, Minkowski functional… Shape metrics estimate sharpness, compactness, sphericity, edge… These metrics can be combined and correlated with classical radiological semantic assessment (for instance, peri-tumoral edema, pattern of enhancement after injection, speculation…) and with other patient demographics, pathological (from initial biopsy) or molecular data (genomics, transcriptomics, proteomics) in order to finally build siganture for tumour diagnosis, tumour aggressiveness, tumour sensibility to specific treatment, patient prognosis. Radiomics approcach can be applied on the whole tumour, on tumour sub-compartments, on surrounding tissue and on apparently healthy tissue [START_REF] Limkin | Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[END_REF][START_REF] Gillies | Radiomics: Images Are More than Pictures, They Are Data[END_REF][START_REF] Kumar | Radiomics: the process and the challenges[END_REF]Lamblin et al., 2012). ) shows the semi-automatically segmented volume, on which shape analysis will be performed. This volume is propagated on co-registered MRI, diffusion weighted MRI, dynamic contrast enhanced MRI maps, 18-FDG-PET CT images in order to perform 1st order, 2 nd order and higher order radiomics analyses, alone, combined bewteen them and finally with clinical and biological data. (C) shows an example of histogram based analysis on T2-wi and K trans maps demonstrating increased T2 heterogenity reflecting oedema, fibrotic and necrotic processes already viewable measurable at t 1 (throughg texture indices) as well as decreased K trans heterogeneity, reflecting the reduction of tumour vascularization, which is even more marked at late evaluation. (C) exhibits a representation of gradient-related parameter superimposed on axial slice of the tumour, in order to evaluate NAC-resistant compartment in a sub-region analysis. (D) shows a example of radiomics indices that varied from t 0 to t 1 .

Texture analysis to identify fibrosis and necrotic pattern (Figure 15)

:

  In combination with MR-morphometrics features, DCE-MRI improves response prediction to a-NAC, correlates with % residual viable tumour cells on post-a-NAC surgical specimen, highlights a 'late-responders' profile. Estimators of fibrosis and necrosis should be added to the model. Prospective multicentre studies are required for validation. * KEYWORDS Soft-tissue sarcomas; neoadjuvant chemotherapy; DCE-MRI; MRI; treatment response KEYPOINTS -DCE-MRI metrics may help to early predict histologic response to a-NAC but further prospective multicentre investigations are required.

Figure 1 .

 1 Figure 1. Features of post-NAC post-operative pathological analysis (A) and conventional MRI correlates (B). Fibrosis (green arrow) is characterized by low SI on T1 and T2-wi with late, ill limited, moderately intense enhancement after CA injection. Necrosis (orange arrow) is characterized by fluid-like low SI on T1wi, very high SI on T2-wi, and no enhancement after CA injection. Viable tumour usually show intermediate SI on T1-wi, T2-wi and got enhanced on static post-contrast sequences.

  (i) Qualitative analysis that mainly relies on estimation of enhancement at the first arterial passages and qualification of the enhancement curve of a region or volume of interest (ROI and VOI, respectively) as a function of time, (ii) Semi-quantitative or model-free analysis, which relies on numeric indices extracted directly from the time-versus-signal intensity (or time-versus-Gadolinium) curve (wash-in, wash-out, area under curve, time to peak of enhancement, Peak enhancement, curve wash-out, signal enhancement ratio), (iii) Quantitative analysis based on permeability multi-compartment pharmacokinetic models. There are pre-requisite before performing this analysis. Indeed, signal intensity variation following CA IV injection is not linearly correlated with CA concentration. To address this issue, it has been proposed to complete a pre-contrast T1 map of the explored volume (Relaxometry or T1 mapping).To correctly convert SI during time, another requirement is classically to calculate a patient-specific arterial input function. The most commonly used and recommended model is the extended Tofts model , a two compartments model (intravascular..

Figure 2 .

 2 Figure 2. Summary of DCE-MRI analyses

Figure 3 .

 3 Figure 3. Study design

Figure 4 .

 4 Figure 4. Pattern of fibrosis on conventional T2-wi. (A) Peripheral fibrosis : apparition of a thick coating with low SI on T2-wi (white arrow). (B) Diffuse entire fibrosis of a grade III leiomyosarcoma (confirmed on pathological analysis where %fibrosis = 95%) (C) Compartmental fibrosis in a poor responders. The anterior component of this grade III UPS showed a decrease of SI on T2-wi compatible with fibrosis (white arrow), while it initially demonstrated similar SI (dotted line) to the posterior, apparently-resistant-nodule. (D) Septa fibrosis : a mesh of severable septa of low T2-SI appeared within a necrotic compartment of a grade III myxofibrosarcoma.

Figure 5 .

 5 Figure 5. DCE-MRI data sets available in order to place the regions of interest (ROI) (A), including raw DCE-MRI (upper images) to identify which was the first area of the tumour to firstly show CA uptake. (B) Three ROI were manually drawn: one circular of at least 1cm 2 on this area (ROI N , red), one on the maximal surface of the tumour (ROI S , yellow), and one on healthy muscle from another lodge. (C) shows the curves for each ROI with corresponding colours (black arrow: time to the peak enhancement, arrow head: t=90s for AUC90)

  Collinearity diagnostic was previously tested. The adequation of the multivariate model was assessed with Hosmer-Lemeshow test and with R 2 -nagelkerke index. The ROC curve of the multivariate model at t1 was built and compared with ROC curves of % change of maximal size (RECIST 1.1) and each individual relevant DCE-MRI 1 parameters.

Figure 6 .

 6 Figure 6. Measurements. (A) longest diameter L1, bi-dimensional diameter 'axb' and volume) at t 0 , t 1 , t 2and change from t 0 to t 1 and t 0 to t 2 . * p<0.05. (B) RECIST1.1 classification at early and late evaluation (PR=partial response, SD=stable disease, PR=progressive disease). Green and red frames correspond to true GHR and true PHR respectively. (C) Area under the ROC curves for %change of longest diameter, bidimensional diameter and volume at early evaluation (t 0 to t 1 ) (not significant p>0.05)

Figure 7 .

 7 Figure 7. Values (at t0 t1 and t2) Variation (from t0 to t1 and 1 to t2) of DCE-MRI semi-quantitative and quantitative indices. * p<0.050, ** p<0.010.

Figure 8 .

 8 Figure 8. ROC curves at early evaluation of % change of the longest diameter, DCE-MRI predictors alone (WIn-ratio-ROI S,1 , PE-ROI S,1 ) and of the multivariate model.

Figure 9 .

 9 Figure 9. RE-differentiation pattern of response of M/RC-LPS under a-NAC. (A) shows conventional MRI at each evaluation point. Maximal diameter did not change. Tumour transverse axis tended to decrease. Note the apparition of additional fatty component within tumour periphery (high SI on T1-wi, low SI on post-contrast fat suppressed T1-wi). (B) Selection of DCE-MRI raw images (prior toCA injection and at arterial, venous and late phases), shows patchy venous and late enhancements, with disappearing of some enhanced area on DCE-MRI 2. (C) Curves at early and late evaluation: at t 1 , ROI Ncorresponding curve (red) was classified type-1, that is to say 'aggressive' compared with muscle curve (blue). At t 2 , the curve was classified type-0. Altogether, DCE-MRI data were compatible with a late good response, but tumour was pathologically classified as PHR because of >10% viable cells despite no round cells.

Figure 10 .Figure 11 .

 1011 Figure 10. Two cases of ill classified true-good histologic responders for two different reasons.(A) Is this STS well-responding? Is this a late-late responder? Follow up demonstrates persistence of a tissue nodule with strong and early enhancement (red arrow head and red curve) while the posterior nodule showed fibrotic process followed by retraction (green arrow head) (B) Is this STS a late responder? At t 1 , rare fibrotic septa appeared. At t 2 , tumour only displayed fibrosis (green arrow head) and necrosis (white arrow head). Pathological analysis reported no residual tumour cells and only fibro-necrotic tissue (note that because of movement artefacts due to breathing, patient was removed from quantitative DCE-MRI studies)

Figure 12 .

 12 Figure 12. Scatterplot of the continuous variables included in multivariate linear regression model at preoperative t 2 . (Dotted green line: included variable but not significant when individually considered. Green line: significant correlations on univariate and multivariate analyses.)

Figure 13 .DISCUSSION 1 .

 131 Figure 13. Two different patterns of oedema on conventional MRI. 'Tumoral oedema' (in a PHR) refers to classically described oedema, with surrounding area of increased SI on T2-wi (black arrow) and focal enhancement of the oedema (orange arrow), suspected to reflect tumour cells deposits in surrounding tissue and hypothesized to have poorer diagnosis. 'Peri-granulation inflammation' corresponds to less-extended oedema (white arrow on coronal fat suppressed T2-wi). Ill-defined enhancement of the oedema can be seen at the whole tumour surface, abutting a 'black rim' of peripheral fibrosis. Pathological analysis on surgical specimen confirmed a good response with clean margins.

Figure 14 .

 14 Figure 14. Ktrans mismatch. (A) Ktrans map after applying Tofts-Kety model: a 0 value was attributed to each voxel within the tumour (deep+superficial grade III synovialosarcoma of the intern side of the right ankle).(B) However, the raw data, the curve and all the other maps were coherent with an entirely hypervascularized tumour (100% of tumour volume enhanced at the first volume acquisition following CA injection). We believe that miscalculation was due to the combined effect of massively hyper-vascularized effect at distant, under-sampling (temporal resolution = 8-9s) and poorly perfused distal location.

Figure 15 .

 15 Figure 15. Radiomics process applied to a STS (grade III UPS) under a-NAC. (A) displays the MRI datasets : t 0 : prior to treatment, t 1 : at early evaluation after two cycles of a-NAC and t 2 : at late pre-surgery evaluation (only two sequences among the whole protocole are shown here for the purpose of this figure, that is to say : static T1 fat-Sat weighted images (wi) post-contrast agent injection and T2-wi). (B) shows the semi-automatically segmented volume, on which shape analysis will be performed. This volume is propagated on co-registered MRI, diffusion weighted MRI, dynamic contrast enhanced MRI maps, 18-FDG-PET CT images in order to perform 1st order, 2 nd order and higher order radiomics analyses, alone, combined bewteen them and finally with clinical and biological data. (C) shows an example of histogram based analysis on T2-wi and K trans maps demonstrating increased T2 heterogenity reflecting oedema, fibrotic and necrotic processes already viewable measurable at t 1 (throughg texture indices) as well as decreased K trans heterogeneity, reflecting the reduction of tumour vascularization, which is even more marked at late evaluation. (C) exhibits a representation of gradient-related parameter superimposed on axial slice of the tumour, in order to evaluate NAC-resistant compartment in a sub-region analysis. (D) shows a example of radiomics indices that varied from t 0 to t 1 .

  All tests were two-tailed. Statistical analyses were done using GraphPad Prism (GraphPad Software, Inc., version 7, La Jolla, CA) and the SPSS statistical package (IBM, version 21.0, Chicago, IL) with Python plug-in. A p-value of less than 0.05 was deemed significant.

	*
	RESULTS
	1. Clinical patient data (Table 1)
	From May 2012 to December 2016, 46 patients (18 female) met inclusion criteria and completed
	conventional MRI0 and DCE-MRI1, 23/46 (50%) also having DCE-MRI 0 and 32/46 (69.6%) DCE-
	MRI 2 . Thus, among them, 23/46 (50%) had both DCE-MRI 0 and DCE-MRI 1 and 17/46 (37.0%) had
	DCE-MRI 0 and DCE-MRI 2 .

Table 1 :

 1 Clinical Features of the study population

		GHR (n = 12)	PHR (n = 34)	Total
	Age (years)	55.5 (39-71)	58 (33-77)	58 (33-77)
	Gender
	-F	1/12 (%)	17/34 (50%)	18/46 (39.1%)
	-M	11/12 (%)	17/34 (50%)	28/46 (60.9%)
	ASA score
	-1	4/12 (33.3%)	12/34 (35.3%)	16/46 (34.8%)
	->1	8/12 (66.6%)	22/34 (64.7%)	30/46 (65.2%)
	Initial pain
	-yes	7/12 (58.3%)	21/33 (61.8%)	28/45 (62.2%)
	-no	5/12 (41.7%)	12/33 (35.3%)	17/45 (37.8%)
	Site group
	-Shoulder girdle	2/12 (16.7%)	3/34 (8.8%)	5/46 (10.9%)
	-Upper limn	2/12 (16.7%)	4/34 (11.8%)	6/46 (13%)
	-Trunk wall	2/12 (16,.%)	2/34 (5.9%)	4/46 (8.7%)
	-Pelvic girdle	1/12 (8.3%)	1/34 (2.90%)	2/46 (4.3%)
	-Lower limb	5/12 (41.7%)	24/34 (70.6%)	29/46 (26.1%)
	Depth
	-Superficial	0/12 (0%)	2/34 (5.9%)	2/46 (4.3%)
	-Superficial and deep	4/12 (33.3%)	8/34 (23.5%)	12/46 (26.1%)
	-Deep	8/12 (66.6%)	24/34 (70.6%)	32/46 (69.6%)
	Histotypes -Undifferentiated sarcomas 1	6/12 (50%)	15/34 (44.1%)	21/46 (45.7%)
	-Muscular sarcomas 2	3/12 (25%)	8/34 (23.5%)	11/46 (23.9%)
	-M-RC LPS 3	0/12 (0%)	6/34 (17.6%)	6/46 (13%)
	-Other LPS	2/12 (16.7%)	1/34 (2.9%)	3/46 (6.5%)
	-Synovialosarcoma	1/12 (8.3%)	3/34 (8.8%)	4/46 (8.7%)
	-MPNST	0/12 (0%)	1/34 (2.9%)	1/46 (2.2%)
	Delay last cycle of a-NAC to	29.5 (21-39)	29 (19-81)	29 (19 -81)
	surgery (days)

OMS and volume (Table 2, Figure 6)

  Initial metrics. There was no significant difference between GHR and PHR at baseline for maximal length, 'a x b' and Volume. t 0  t 1 . Percentages of maximal length, 'a x b' and volume change (%Change-L1 0  1 , %Changeaxb 0  1 , %Change-Vol 0  1 ) did not exhibit significant difference either from MRI 0 to MRI 1 . According to OMS, 4/34 (11.8%) PHR were ill-classified as partial response, 6/34 (17.6%) were well classified as progressive disease, 24/34 (70.6%) PHR were classified as stable disease.t 0 t 2 . At final MRI 2 evaluation, %Change-L1 0  2 , %Change-axb 0  2 and %Change-Vol 0  2 were all significantly decrease in GHR compared withPHR (p=0.012, p=0.010, p=0.031, respectively)

	Thus, according to RECIST1.1, 3/34 (8.8%) PHR were ill-classified as partial response, 3/34
	(8.8%) PHR were well-classified as progressive disease, and 28/34 (82.4%) were classified as stable
	disease.

  t 0  t 1 . From MRI 0 to MRI 1 , there was a tendency towards a decrease of the initial peripheral oedema, without reaching significance (p=0.054).

	Distribution of %fibrosis was significantly different between PHR and GHR (p=0.008) with 1/34
	(2.9%) PHR who had qualitatively more than 50% of its volume exhibiting a MRI signal
	compatible with fibrosis versus 4/12 GHR (33.3%). 11/34 PHR (32.4%) showed a compartmental
	fibrosis pattern, versus only 1/12 (8.3%) GHR. Despite no significance, this observation suggests
	that occurrence of a growing tissue nodule within a tumour tending to get fibrotic should suggest a
	poor response.

  Table 4 and Figure7summarize the results for each parameter for each time point. Overall, at t 1 , WIn-ratio-ROI n was significantly lower for GHR than

	for PHR (4.78 1.19 versus 9.8  1.65, p=0.048), as well as PE-ROI n (93.43 25.62 versus 166.9 
	96.72, p=0.029). At t 2 , WIn-ratio-ROI n and PE-ROI n were still significantly lower for GHR

(p=0.022 and p=0.023, respectively), as well as iAUC90-ratio-ROI n (p=0.020)

Table 3 :

 3 Conventional MRI features at first evaluation t1 ROI s , K trans -ROI n , K trans -ratio-ROI s , K trans -ratio-ROI n (p=0.043, p=0.009, p=0.021 and p=0.009, respectively) Determination of optimal threshold for significant criteria at t 1 . For WIn-ratio-ROI n , ROC analysis demonstrated an AUC=0.70 (0.53-0.87). Optimal threshold, was 3.48 mmol.L -1 .s -1 (Youden index = 0.03) leading to Se=0.79, Sp=0.55, PPV=0.84, OR univariate=5.40 (1.17, 23.72) to detect PHR.For PE-ROI n , AUC was 0.71 (0.54-0.89). Optimal threshold was 143.5%. (Youden index = 0.49) providing Se=068, Sp=0.83, PPV=0.92, OR univariate=10.45 (1.99-51.22) to detect PHR.

	PHR	GHR	p-value

Table 6 :

 6 Synthesis of the Multivariate binary logistic regression results to predict pathologic response at early evaluation t 1 . (A) OR with their 95% IC and p-value, estimated after univariate and multivariate analyses. (B) Patient's classification according to the model. Note that one of the initial 46 patients had too poor quality DCE-MRI 1 to compute the semi-quantitative and quantitative parametric maps and was removed from this analysis. (C) Area under curve of ROC curve of the model, %Change of maximal axis

	(L1) from t 0 to t 1 , PE-ROI s,1 and WIn-ratio-ROI s,1 with standard error, 95% IC and p-value
	A.
			OR -univariate analysis	OR -multivariate analysis
	Gender	11 (1.42 ; 124.60) p=0.015 *	8.04 (0.82 ; 79.09) p=0.074
	PE-ROI s,1	10.45 (0.198 ; 51.22) p=0.006 **	5.19 (0.76 ; 35.35) p=0.092
	Win-ratio-ROI s,1	5.4 (1.17 ; 23.72) p=0.026 *	2.25 (0.38 ; 13.29) p=0.371
	B.
	True-PHR	True-GHR
	Model +	32	5	37
	Model -	2	6	8
		34	11	45
	C.
			AUC	Standard error	95% IC
	0%Change-L1		0.608	0.105	(0.403 ; 0.814)
	Win-ratio-ROI s,1		0.701	0.090	(0.525 ; 0.876)
	PE-ROI s,1		0.711	0.089	(0.536 ; 0.886)
	Multivariate Model	0.836	0.081	(0.677 ; 0.994)

Table 7 .

 7 Uni and Multivariate Linear Regression between relevant continuous variables and % at t 2

			Univariate	Multivariate
		r	p	r	p
	%Change-L1 02 (N=32)	0.410	0.020 *	0.370	0.022 *
	%Change-'axb' 02 (N=32)	0.447	0.010 *	_	_
	%Change-Volume 02 (N=32)	0.347	0.035 *	_	_
	WIn-ratio-ROI n,2 (N=32)	0.489	0.005 **	0.394	0.029 *
	iAUC90-ratio-ROI n,2 (N=31)	0.570	<0.001 ***	-	-
	K trans-ratio-ROI n,2 (N=27)	0.562	0.002 **	0.202	0.251
	PE-ROI n,2 (N=32)	0.287	0.111	_	_
	The predictors included in the multivariate anlaysis were : %change-L1 02, Win-ratio-ROI n,2 K trans-
	ratio-ROI n,2.

Work in progress 

  Texture analysis (1 st order and 2 nd order statistics, taking into account MRI qualitative features and clinical data), with machine learning approach, on T2 sequences is currently being performed (N =90) in order to identify numeric indices at t 1 that are correlated with histologic response and prognosis. MRIs from 60 patients are being processed to investigate the value of changes of the indices from t 0 to t 1 . Patients who also benefited from MRI at t2 will help to build a mathematical model to predict morphometrical local evolution. Patients from Neosarcomics trial will be included in a validation study. 30 patients from the ongoing texture study are also part of this present DCE-MRI study. We will investigate, after selection and/or creation of composite indices, if additional relevant texture features could improve the prediction model for histologic response.ConclusionDCE-MRI has been insufficiently studied to assess STS response under NAC. Overall, our results demonstrate potential early predictors of response among DCE-MRI numeric indices, prior to size variation, RECIST1.1 or OMS, this with acceptable reproducibility. Additionally, MRI could help to identify resistant tumours, late responders, original de-differentiation pattern in M/RC-LPS and potential immune inflammation surrounding good responders, requiring further radio-pathological correlations studies. Prospective multicentre studies are required for validation.

Prospects

 Ancillary study from Neosarcomics is planned to build a radiomics-based signature for prognosis, prediction of treatment response and identification of radiomics phenotypes correlated with targetable molecular alterations. *

undifferentiated epitheloid/spindle cell/pleomorphic sarcoma, undifferentiated sarcoma not otherwise specified ;

rhabdomyosarcoma, leimyosarcoma ;

dedifferentiated LPS, pleomorphic LPS LPS : liposarcoma, M-RC LPS : myxoid-round cell LPS ; MPNST : malignant peripheral nerve sheath tumour

SERMENT MEDICAL

Au moment d'être admise à exercer la médecine, je promets et je jure d'être fidèle aux lois de l'honneur et de la probité.

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