AI and Cancer Research ── Brain ── 2026-11-05
Brain tumours: treatment, trials and AI
- Abstract
Accurate prediction of patient outcomes remains a critical challenge in oncology, in particular for childhood brain tumors, in which treatment decisions need to carefully balance risk of tumor recurrence and death against severe therapy-induced sequelae. Here, we developed MANTIS (methylation profiling and artificial neural networks for time-dependent individualized survival predictions), a precision artificial intelligence (AI) tool for medulloblastoma and ependymoma outcome prediction built on genome-wide DNA methylation array data from 2,786 patients. MANTIS generates individualized survival probabilities over a 10-year period and achieves concordance indices as high as 0.80 ± 0.07, significantly exceeding the predictive accuracy of current clinical indicators. Extending MANTIS to estimate individualized treatment effects identifies ~60% of patients with average-risk medulloblastoma without a significant survival disadvantage (P = 0.683) when treated with less intensive craniospinal irradiation doses, which is associated with improved neurocognitive outcomes. In conclusion, our study establishes a foundation for AI-based precision prognostics and therapy personalization in childhood brain tumors and offers a blueprint for similar initiatives in other cancer types.
Journal IF-equivalent: 19.6 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-05; not the official Clarivate IF)Reference: Stefan S, Amann AS, Smith KS, Boniolo F, Larsson I, Pfitzer EC, et al. AI-based precision prognostics and therapy personalization for childhood brain tumors. Nat Genet. 2026 Nov 3 [Epub ahead of print]. doi:10.1038/s41588-026-02815-4. PMID: 42977823.Checked: Abstract only - Abstract
BACKGROUND: Meningioma surgery aims to preserve neurological function, usually measured by the Karnofsky Performance Scale (KPS), but predicting postoperative KPS change remains challenging. METHODS: We retrospectively analyzed 397 meningioma resections from 14 Italian centers. Preoperative clinical and MRI features (age, tumor size, eloquent-area location, vessel and cranial-nerve involvement, prior surgery, baseline KPS, etc.) were used to train classifiers (logistic GAM, weighted logistic regression, linear/RBF SVM, random forest) with cross-validation. We compared performance to the Staartjes et al. GAM_MCS model (predicting KPS drop ≥10). RESULTS: All models achieved only moderate discrimination for new deficits: AUC ≈0.65-0.70 (Staartjes2 GAM_MCS 0.692; logistic regression 0.694). Predicting major changes was more accurate: for KPS drops ≥20 or ≥30 points, AUC ≈0.76-0.77, and for any postoperative improvement AUC ≈0.81, whereas 10-point declines were hardest (AUC ≈0.70). For example, weighted logistic regression yielded AUCs of 0.697, 0.765, 0.762, and 0.811 for predicting KPS drops ≥10, ≥20, ≥30, and any improvement, respectively. The most important predictors were anatomical complexity factors: eloquent-area involvement, major vessel manipulation, and tumor size (consistent with the "Big Five" risk factors). CONCLUSIONS: Preoperative anatomical complexity remains the dominant predictor of postoperative functional outcome. Logistic-based ML classifiers had moderate overall accuracy but better performance for extreme outcomes (major decline or clear improvement). Using a higher threshold (e.g. ≥20-point KPS drop) improved discrimination and aligns with clinically meaningful outcome definitions. These findings support emphasizing high-impact anatomical features and outcome thresholds to improve prognostic models.
Journal IF-equivalent: 1.4 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-05; not the official Clarivate IF)Reference: MORETTI M, MALTONI D, CIOCCA R, BROGGI M, SCHIARITI M, RESTELLI F, et al. Five predictors of meningioma surgery: an AI analysis. J Neurosurg Sci. 2026 Nov;70(5). doi:10.23736/s0390-5616.26.06696-8. PMID: 42974822.Checked: Abstract only