T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

Abstract

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.

Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

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Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

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Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

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