Probability-Level Fusion of Meta-Information and Multimodal MRI/PET Radiomics for Predicting 1p/19q Codeletion and IDH Mutation in Gliomas

Noninvasive prediction of 1p/19q codeletion and isocitrate dehydrogenase (IDH) mutation may support preoperative assessment of lower-grade gliomas. We evaluated leakage-aware probability-level fusion of meta-information and multimodal radiomics. This retrospective study included 217 patients with World Health Organization grade II or III gliomas and preoperative magnetic resonance imaging and positron emission tomography. Meta-information and radiomics models were developed separately, and their calibrated probabilities were combined using weighted averaging or logit stacking. Performance was evaluated using repeated stratified five-fold nested cross-validation with 10 seeds, with all data-dependent procedures confined to training data. For 1p/19q codeletion, weighted averaging achieved an outer-fold area under the receiver operating characteristic curve (AUC) of 0.816 ± 0.056 (mean ± standard deviation), and both fusion approaches achieved higher patient-level AUCs than meta-information alone after Holm correction. For IDH mutation, weighted averaging achieved an AUC of 0.799 ± 0.077 and a higher patient-level AUC than radiomics alone, whereas the comparison with meta-information alone was not statistically supported. Modality-group analyses revealed task-dependent performance patterns without identifying a consistently superior modality condition after multiplicity correction. Probability-level fusion showed task-dependent incremental discrimination and warrants further external and prospective evaluation with contemporary molecular reference standards and prespecified imaging harmonization procedures.