Inflammation-lipid metabolism biomarker-based machine learning models for predicting postoperative pulmonary metastasis in colorectal cancer

Table 3 External validation (Gansu Provincial Cancer Hospital, Center B): Performance comparison across models

Algorithm	Type	Ext_AUC	Ext_AUC_95CI	Int_AUC	AUPRC	Brier	ABscore	Accuracy	Sensitivity	Specificity	F1	PPV	NPV	Threshold (from internal validation)
Stacking	Ensemble	0.7127	0.6533-0.7722	0.732	0.4476	0.1857	0.8881	0.6955	0.6796	0.702	0.5622	0.4795	0.8443	0.3221
Weighted Voting	Ensemble	0.7112	0.6498-0.7702	0.7498	0.439	0.185	0.9734	0.7039	0.699	0.7059	0.576	0.4898	0.8531	0.3507
Soft Voting	Ensemble	0.7111	0.6495-0.7699	0.7499	0.4389	0.185	0.9712	0.7067	0.699	0.7098	0.5783	0.4932	0.8538	0.3512
LightGBM	Single	0.7087	0.6492-0.7664	0.7507	0.4331	0.1863	0.725	0.676	0.7087	0.6627	0.5573	0.4591	0.8492	0.3144
GBDT	Single	0.7075	0.6473-0.7699	0.7367	0.4452	0.1874	0.528	0.648	0.7476	0.6078	0.55	0.435	0.8564	0.2922
RF	Single	0.6961	0.6352-0.7541	0.7252	0.4168	0.1882	0.204	0.6369	0.7476	0.5922	0.5423	0.4254	0.8531	0.3333
RotF	Single	0.6848	0.6228-0.7436	0.7454	0.4384	0.188	0.037	0.6732	0.6893	0.6667	0.5483	0.4551	0.8416	0.3459

AUC: Area under the curve; AUPRC: Area under the precision-recall curve; PPV: Positive predictive value; NPV: Negative predictive value; RotF: Rotation Forest; GBDT: Gradient Boosting Decision Tree; RF: Random Forest.