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In addition to tumor size, the French Federation of Cancer Centers histologic grading system is the most important prognostic factor in soft-tissue sarcoma (STS). The French Federation of Cancer Centers grading system considers tumor differentiation, mitotic activity, and necrosis (1). Several studies have validated its predictive value for local recurrence-free survival, metastasis-free survival, and overall survival (2-5). Because anthracycline-based neoadjuvant chemotherapy improves prognosis in patients with locally advanced deep-seated grade III STS, grade is routinely assessed to support its indication (6-9). Grade was historically validated on complete surgical specimens; however, imaging-guided core-needle biopsies have now become standard in the diagnosis of STS (10,11). Therefore, due to tumor heterogeneity, the initial grading performed on biopsy samples may underestimate the final grade rendered on the surgical specimen (12-14).Two approaches could be used to limit grade underestimation. The first approach is the identification of high-grade areas at imaging to guide biopsies. Previous studies have investigated the relationship between imaging features and grade by using conventional MRI, radiomics analysis of diffusion-weighted imaging, and fluorine 18 fluorodeoxyglucose PET/CT; these studies have identified associations between glycolytic metabolism, tumor heterogeneity, growth pattern, and grade (14-19). However, direct voxel-to-voxel comparisons of histologic grades and potential imaging features are lacking. In practice, many patients who present with a soft-tissue mass are referred to sarcoma reference centers with a conventional MRI examination already performed out of the center. Furthermore, to optimize the biopsy, radiologists purposely avoid targeting necrotic areas, which may lead to an underestimation of the amount of necrosis.

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Background Standard of care for patients with high‐grade soft‐tissue sarcoma (STS) are being redefined since neoadjuvant chemotherapy (NAC) has demonstrated a positive effect on patients' outcome. Yet response evaluation in clinical trials still relies on RECIST criteria. Purpose To investigate the added value of a Delta‐radiomics approach for early response prediction in patients with STS undergoing NAC. Study Type Retrospective. Population Sixty‐five adult patients with newly‐diagnosed, locally‐advanced, histologically proven high‐grade STS of trunk and extremities. All were treated by anthracycline‐based NAC followed by surgery and had available MRI at baseline and after two chemotherapy cycles. Field Strength/Sequence Pre‐ and postcontrast enhanced T1‐weighted imaging (T1‐WI), turbo spin echo T2‐WI at 1.5 T. Assessment A threshold of <10% viable cells on surgical specimens defined good response (Good‐HR). Two senior radiologists performed a semantic analysis of the MRI. After 3D manual segmentation of tumors at baseline and early evaluation, and standardization of voxel‐sizes and intensities, absolute changes in 33 texture and shape features were calculated. Statistical Tests Classification models based on logistic regression, support vector machine, k‐nearest neighbors, and random forests were elaborated using crossvalidation (training and validation) on 50 patients ("training cohort") and was validated on 15 other patients ("test cohort"). Results Sixteen patients were good‐HR. Neither RECIST status (P = 0.112) nor semantic radiological variables were associated with response (range of P‐values: 0.134–0.490) except an edema decrease (P = 0.003), although 14 shape and texture features were (range of P‐values: 0.002–0.037). On the training cohort, the highest diagnostic performances were obtained with random forests built on three features: Δ_Histogram_Entropy, Δ_Elongation, Δ_Surrounding_Edema, which provided: area under the curve the receiver operating characteristic = 0.86, accuracy = 88.1%, sensitivity = 94.1%, and specificity = 66.3%. On the test cohort, this model provided an accuracy of 74.6% but 3/5 good‐HR were systematically ill‐classified. Data Conclusion A T2‐based Delta‐radiomics approach might improve early response assessment in STS patients with a limited number of features. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:497–510.