Figure 3.

The development and validation of radiomic models predicting the intracranial efficacy of the second-line osimertinib. (A) Eight feature selectors and eight classifier machine learning algorithms were used to construct radiomic models. A nested 5-fold cross-validation strategy was applied. Eventually, 64 (8 × 8) combinations of radiomic feature selectors and classifiers were generated. The matrix displays the highest AUCs of ROC for the 5-fold validation. (B) Visualization of the optimal model constructed by the mRMR feature selector combined with the stepwise logistic regression classifier. (C–K) Model validation of the training (C–E), validation (F–H), and total cohort (I–K) using ROC curves, DCA, and calibration curves. ROC, receiver operator characteristic curve; DCA, decision curve analysis; AUC, area under the curve.