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Abstract #2677

Preoperative Pathological Differentiation of Grade 1 and Grade 2/3 Soft Tissue Sarcomas based on Radiomics of ADC maps

Yu Zhang1, Shaowu Wang1, Yuwei Xia2, and Kai Zhang1
1The second hospital of Dalian Medical University, Dalian, China, 2Huiying Medical Technology Inc, Beijing, China

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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