Exploring Deep Learning and Hybrid Approaches in Molecular Subgrouping and Prognostic Related Genetic Signatures of Medulloblastoma

Objectives Deep learning (DL) based on MRI of medulloblastoma enables risk stratification, potentially aiding in therapeutic decisions. This study to develop DL models that identify four medulloblastoma molecular subgroups and prognostic related genetic signatures. Materials and Methods In this retrospective study, consecutive patients with newly diagnosed MB at MRI (T1-, T2- and contrast-enhanced T1-weighted) at two medical institutes between January 2015 and June 2023 were identified. Two-stage sequential DL models were designed—MB-CNN that first identifies wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. Further, prognostic related genetic signatures DL models (MB-CNN_TP53/MYC/Chr11) were developed to predict TP53 mutation, MYC amplification, and chromosome 11 loss status. A hybrid model combining MB-CNN and conventional data (clinical information and MRI features) was compared to a logistic regression model constructed only with conventional data. Four-classification tasks were evaluated with confusion matrices (accuracy) and two-classification tasks with ROC curves (area under the curve, AUC). Results The datasets comprised 449 patients (mean age ± SD at diagnosis, 13.55 years ± 2.33, 249 males). MB-CNN accurately classified MB subgroups in the external test dataset (accuracy was in the range of 76.29–78.71%). MB-CNN_TP53/MYC/Chr11 models effectively predicted signatures (AUC of TP53 in SHH: 0.91, MYC amplification in Group 3: 0.87, chromosome 11 loss in Group 4: 0.89). The accuracy of hybrid model outperformed the logistic regression model (82.20% vs. 59.14%, P  = .009) and showed comparable performance to MB-CNN (82.20% vs. 77.50%, P  = .105). Conclusion MRI-based DL models allowed identification of the molecular medulloblastoma subgroups and prognostic related genetic signatures.