https://www.selleckchem.com/pr....oducts/cid-1067700.h
727, 0.873, 0.674, and 0.888 in the training dataset, respectively, and 0.706, 0.829, 0.643, and 0.875 in the validation dataset, respectively. The predictive performance of mp-MRI classification model in the AUC value was significantly better than that of the individual sequence model (all p less then 0.01). CONCLUSION In clinical practice, a noninvasive approach to improve the performance of radiomics in preoperative prediction of Ki-67 status can be provided by extracting breast cancer specific structural and functional features
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