Introduction: Three-dimensional deep learning offers promise for automated accurate brain tumor segmentation and survival prediction but requires robust validation across multiple MRI modalities to be effectively implemented in clinical practice. Methods: This study presents a comprehensive 3D deep learning framework using 369 cases from the BraTS2020 dataset. A 3D U-Net architecture was developed for tumor segmentation utilizing combined imaging data and optimized for computational efficiency and memory. The final 3D U-Net model segmentations were used to build machine learning 6-month and 12-month survival classifiers. Segmentation models were evaluated using multiple metrics, including the Dice Similarity Coefficient, Hausdorff Distance, and Cohen’s d. The classification models were evaluated using AUC-ROC and balanced accuracy. Results: Segmentation achieved a modest, but promising, performance across 30 epochs and with 295 training patients, achieving the best mean validation Dice = 0.8388 and a final-epoch mean Dice of 0.8263. Survival classification with a hybrid clinical and imaging logistic regression showed promising results, with 12-month prediction achieving AUC = 0.746 and 69% accuracy. The top contributing features for the 12-month prediction classifier were extent of resection, T1 contrast-enhanced tumor median, and FLAIR tumor median. Conclusions: This comprehensive framework demonstrates that a multi-modal approach provides meaningful performance gains, while segmentation-derived features show a promising ability to enable survival prediction.

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes.

Multi-sequence MRI is the standard for glioma assessment, but complete or contrast-enhanced protocols are not always available (withheld gadolinium, low-field MRI, incomplete archives). We asked whether tumor localization improves grade classification from T2-weighted MRI alone. In this retrospective study of the BraTS2020 training cohort (369 patients; legacy labels 293 high-grade [HGG], 76 low-grade [LGG]), 74 patients (15 LGG) were held out at the patient level for internal testing. A 2D U-Net trained in five development folds supplied out-of-fold whole-tumor masks for training 3D CNN classifiers; nnU-Net served as a segmentation benchmark. Classifiers received T2 alone or T2 plus a mask, which at test time came from the 2D U-Net, nnU-Net, the ground truth (oracle) or random noise. Analyses used five seeds, DeLong tests with Holm correction and development-set cross-validation. Whole-tumor Dice was 0.807 (95% CI 0.773–0.840) for the 2D U-Net and 0.877 (0.857–0.895) for nnU-Net. The T2-only CNN reached an ROC-AUC of 0.814 (95% CI 0.702–0.913). Mask-guided variants reached 0.677–0.742, none above T2-only (Holm-adjusted p = 1.00 for all four confirmatory comparisons of informative masks). Cross-validation reproduced this ranking (0.782 vs. 0.707). With LGG as the positive class, PR-AUC was 0.571 (no-skill 0.203). Tumor-mask guidance did not improve T2-only grading at any level of mask quality, including ground truth. The results concern a legacy grade label in one public cohort and require external testing.