Multicenter external validation of a multimodal MRI deep-learning model to predict meningioma bone invasion

Preoperative detection of meningioma bone invasion lacks objective biomarkers and relies on subjective radiological assessment, posing a risk of excessive bone resection or residual tumor. We therefore developed and externally validated a parsimonious six-feature DenseNet121 signature derived from multiparametric MRI across two independent institutions.

In this retrospective diagnostic accuracy study, 438 consecutive patients (mean age, 53 ± 11 years; 77% women) with histologically confirmed meningioma were allocated to training (n = 182), internal validation (n = 77), and external test (n = 179) sets. Following N4 bias-field correction and isotropic resampling, contrast-enhanced T1-weighted, T2-weighted, and diffusion-weighted volumes were fed into a DenseNet121 architecture to extract a six-feature signature via nested cross-validation. Discrimination (area under the ROC curve, AUC), calibration (Hosmer-Lemeshow test), and clinical utility (decision-curve analysis) were evaluated.

In the external cohort (prevalence 11%), the signature achieved an AUC of 0.82 (95% CI 0.73-0.91), sensitivity 75%, specificity 77% and negative predictive value 96%, with an AUC improvement of 0.155 over conventional radiomics (95% CI, 0.01-0.30; p = 0.035). At a 20% risk threshold, decision-curve analysis confirmed a positive net clinical benefit, primarily driven by the high negative predictive value. SHAP analysis demonstrated the dominant and consistent contribution of low-ADC features.

The DenseNet121-derived six-feature signature serves as a reliable rule-out aid for preoperative meningioma bone invasion, with a high negative predictive value that may safely support decisions to avoid extensive bone resection in predicted-negative cases. Positive predictions require confirmatory CT or intraoperative biopsy to mitigate false positives. This tool could assist nurses and surgeons in preoperative planning, facilitating personalized patient care and potentially reducing operative time and complications.