AI and Cancer Research ── Brain ── 2026-11-07
Brain tumours: treatment, trials and AI
- Abstract
Objective: To evaluate contrast-enhanced T1-weighted MRI (CE-T1WI) models for preoperative prediction of isocitrate dehydrogenase (IDH) mutation status in adult-type diffuse gliomas and explore phenotype associations of influential latent features.
Methods: This retrospective study included 462 patients. Center 1 provided training ( n = 259) and internal-test ( n = 65) cohorts, and Center 2 provided external validation ( n = 138). Five input–architecture combinations and six classifiers were evaluated against an interpretable logistic benchmark combining age and Visually Accessible Rembrandt Images (VASARI) descriptors. For each architecture, the classifier with the highest observed external area under the curve (AUC) was selected for reporting. Performance was assessed using discrimination, calibration, decision curves, and 100 downstream refits with fixed deep features. Shapley additive explanations (SHAP) and phenotype association analyses examined influential latent features and their relationships with clinical and imaging phenotypes.
Results: Region-of-interest (ROI)-constrained three-dimensional vision Transformer (3D ViT) with a support vector machine (SVM) achieved an external AUC of 0.929 (95% CI, 0.885–0.973), accuracy of 0.848, sensitivity of 0.838, specificity of 0.857, and Brier score of 0.106. The age–VASARI benchmark achieved an external AUC of 0.782. Across downstream refits, the ROI-constrained model had a mean external AUC of 0.930 and the smallest mean training-minus-external AUC difference (0.019). The same 10 features formed the highest-importance SHAP set across cohorts, with different rank orders. Feature_546 correlated with age ( r = 0.461), postoperative Ki-67 ( r = 0.562), and enhancement quality ( r = 0.678; all adjusted p < 0.001).
Conclusion: ROI-constrained volumetric learning from CE-T1WI showed promising cross-center IDH discrimination using one routine MRI sequence. Influential latent features were associated with postoperative Ki-67 and recognizable imaging phenotypes. SHAP attribution and these exploratory associations provide complementary perspectives on model behavior in preoperative IDH assessment.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Zheng C, Zhang Q, Li D, Wang H, Yang J, Lv F, et al. Explainable volumetric deep learning from contrast-enhanced T1-weighted MRI for preoperative IDH prediction in adult-type diffuse gliomas: a two-center study. Front Neurol. 2026 Nov 4;17:1966591. doi:10.3389/fneur.2026.1966591.Checked: Abstract only - Abstract
OBJECTIVE: To develop ML models predicting postoperative CSF leakage after endoscopic transsphenoidal pituitary adenectomy (TSPA). METHODS: Four hundred and twenty patients from five hospitals (January 2021-December 2022) comprised the training cohort; 140 from another hospital were the validation cohort. The training cohort included 60 CSF leak and 360 nonleak cases. Risk factors were analyzed. Decision tree, logistic regression, and SMOTE-based models were built and their diagnostic value compared. RESULTS: Significant differences ( p < 0.05) existed in tumor diameter, tumor texture, operative time, intraoperative diaphragma sellae rupture, reoperation, and intraoperative CSF leakage grade. Logistic regression identified tumor texture, reoperation, intraoperative CSF leakage grade, and diaphragma sellae rupture as risk factors. All models had high AUCs in training: logistic regression (0.792), decision tree (0.835), and SMOTE (0.839). The Delong test showed a decision tree, and SMOTE outperformed logistic regression. SMOTE had the highest sensitivity/specificity. Validation cohort AUCs were: logistic regression (0.775), decision tree (0.809), and SMOTE (0.831), with SMOTE superior. Calibration curves indicated good agreement between predicted and actual values. CONCLUSION: Prediction models for CSF leakage postendoscopic TSPA were developed using decision trees, SMOTE, and logistic regression. Model efficacy was evaluated via AUC, specificity, and sensitivity. Comparing algorithms reduced single-algorithm bias, with SMOTE demonstrating the best performance.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Shao S, Ye C, Du Y, Liu T, Wu Y, Chen M, et al. Construction of a Predictive Model for Cerebrospinal Fluid Leakage After Endoscopic Transsphenoidal Pituitary Tumor Surgery Based on Machine Learning Algorithm. J Neurol Surg B Skull Base. 2026 Jan 6;87(06):669-77. doi:10.1055/a-2775-5314.Checked: Abstract only