Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study

This study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers. A retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training ( n = 280) and test ( n = 120) cohorts. Spatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively). Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.