XGBoost had a higher accuracy (area under the receiver operator characteristic curve [AUC] = 0.747) than the 2 other classifiers, logistic regression (AUC = 0.701) and random forest (AUC = 0.719).

Using the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited ...