Predictive Performance of Models
With an AUC of 0.999 (95% CI: 0.999-1.000) in the training cohort, the radiomics-random forest model produced the most satisfactory results. The AUC values of the SVM, KNN, and LightGBM models in the training cohort were 0.880 (0.835-0.927), 0.873 (0.829-0.916), and 0.926 (0.892-0.959), respectively (Figure The results of decision curve analysis in the validation and training sets are consistent with their AUC values, with the LightGBM model offering the greatest overall net benefit in the validation cohort and the random forest model providing the greatest overall net benefit in the training cohort (Figure According to the initial results, although the random forest model performs best on the training set, it performs poorly on the validation set and is not stable. While the SVM model, the KNN model, and the LightGBM models have reasonably steady performance in the training set and validation set, the LightGBM model has the best AUC value and the most significant net benefit overall.We compared the radiomics models established by various methods with the ultrasound diagnosis of clinical pathologists. In terms of accuracy, sensitivity, and specificity, we discovered that the radiomics models generated by the four methods performed significantly better than the sonographers' judgment in both the training cohort and the validation cohort (Table 