Radiomics based on ADC maps provides a new evaluation method for the preoperative pathological differentiation of Grade 1 and Grade 2/3 soft tissue sarcomas. By comparing the performance of five classifiers (random forests, logistic regression, Multi-Layer Perceptron, k-nearest neighbor, and support vector machine), we found that random forests model achieved the best result (AUC: 0.802 (95% CI: 0.659-0.881), sensitivity:0.722, specificity:0.875) on ADC maps, that can serve as a quantitative tool to differentiation of Grade 1 and Grade 2/3 soft tissue sarcomas. And the radiomics features have the capability in reflecting the Ki67 index
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