A total of 92 subjects(66 TLE [35 right and 31 left] and 26 healthy controls) were allocated to training(n=66) and test(n=26) sets. Radiomics features (n=558) from the bilateral hippocampi were extracted from T1WI and DTI. Machine learning models were trained. Identical processes were performed to differentiate right TLE from HC and left TLE from HC. The radiomics model in test set showed better performance than hippocampal volume for identifying TLE (AUC 0.82 vs. AUC 0.62, P=0.08). Radiomics models of both subgroups showed better performance than those of hippocampal volume(AUC 0.76 vs. AUC 0.54 [P=0.12] and AUC 0.95 vs 0.68 [P=0.04]).