Artificial intelligence-integrated multimodal data-assisted magnetic resonance imaging for neoadjuvant chemoradiotherapy decision-making in cT1-2N0 rectal cancer

Table 2 Performance comparison of ten machine-learning models trained on multimodal radiomics and clinical data vs radiologist diagnosis

Model	Area under the curve	Accuracy	Sensitivity	Specificity	Positive predictive value	Negative predictive value	F1
Logistic	0.798 (0.739-0.857)	0.817	0.547	0.907	0.661	0.858	0.699
Support vector machine	0.817 (0.764-0.870)	0.744	0.827	0.717	0.492	0.926	0.717
Gradient boosting machine	0.917 (0.881-0.953)	0.904	0.760	0.951	0.838	0.923	0.897
NeuralNetwork	0.798 (0.739-0.857)	0.731	0.733	0.730	0.474	0.892	0.676
RandomForest	0.922 (0.889-0.954)	0.857	0.827	0.867	0.674	0.938	0.843
XGBoost	0.938 (0.911-0.965)	0.870	0.867	0.872	0.692	0.952	0.869
K-nearest neighbors	0.910 (0.874-0.946)	0.787	0.920	0.743	0.543	0.966	0.783
Adaboost	0.747 (0.684-0.810)	0.748	0.680	0.770	0.496	0.879	0.673
LightGBM	0.922 (0.889-0.956)	0.834	0.893	0.814	0.615	0.958	0.728
CatBoost	0.886 (0.845-0.928)	0.807	0.813	0.805	0.581	0.929	0.678
Reader 1	1	0.660	0.760	0.626	0.404	0.923	0.528
Reader 2	1	0.750	0.840	0.720	0.500	0.931	0.627
Reader 3	1	0.790	0.920	0.746	0.548	0.966	0.687

¹The physicians’ diagnostic decisions were recorded as binary categorical judgments (e.g., presence vs absence of disease) rather than as continuous probability scores. Therefore, receiver operating characteristic curve analysis and the corresponding area under the curve were not applicable. Instead, sensitivity and specificity were calculated.