This work aimed for a radiomics based strategy to identify poorly differentiated hepatocellular carcinoma (HCC) which may own a high risk of recurrence or metastasis. By comparing the performance of four classifiers (decision tree, DT; random forest, RF; k-nearest neighbors, KNN; logistic regression, LR) on dual-echo T1WI (in-phase and out-phase), T2WI and DWI images, we found that LR achieved the best result (AUC: 0.95; sensitivity: 0.75; specificity: 0.85) on DWI images, forming a valuable strategy for clinical practice.
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