Explainable radiomics with probability calibration for postoperative glioblastoma surveillance

PurposeTo develop and validate a calibrated, explainable radiomics pipeline for classifying progression status in postoperative glioblastoma (GBM) patients using multiparametric MRI acquired at longitudinal follow-up scans. MethodsWe retrospectively analyzed the MU-Glioma-Post dataset, which includes serial post-treatment MRI (T1, T1-CE, T2, FLAIR).Volumes were corrected with N4, resampled at 1 mm resolution, co-registered to SRI24, skull-stripped, and segmented using nnU-Net into categories such as enhancing tissue, non-enhancing tumor core, pericavitary FLAIR hyperintensity, and resection cavity.Expert neuroradiologists refined these segmentations to ensure precise delineation for the extraction of radiomic features.Radiomics features were extracted on Pyradiomics, Laplacian-of-Gaussian ( = 0.5-3.0mm), and 3D wavelet sub-bands.Stability-aware ranking (variance/correlation filters, L1logistic, permutation importance) was performed before classifier training.A LightGBM model was optimized with patient-aware 5-fold cross-validation and Optuna tuning, then Platt-calibrated on out-of-fold scores.Performance was assessed on a patient-held-out test set using AUC, confusion matrix, and Brier score, while SHAP provided cohort-level explanations. ResultsThe LightGBM model trained on 256 radiomics features achieved an AUC of 0.80 on the held-out patient test set, with a confusion matrix indicating sixteen false positives and six false negatives.Calibration improved the Brier score from 0.093 to 0.088.Global explanations showed that the model mainly relied on coarsescale wavelet/LoG GLCM/GLSZM textures across all modalities, with little dependence on shape features.Riskincreasing attributions were often due to enhancing rim on T1-CE and pericavitary FLAIR textures. ConclusionsA calibrated, explainable radiomics model trained on longitudinal post-operative MRI provides strong discrimination and well-calibrated probabilities for GBM progression prediction, supporting threshold-based decision-making in surveillance.Radiomic signatures emphasizing coarse-scale heterogeneity and enhancing texture patterns were most strongly associated with earlier progression.External, multi-centre validation and evaluation of diffusion/perfusion and delta-radiomics are warranted to establish generalizability.