3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset.

Overview

1. Department of Neurosurgery, Stanford University, Stanford, CA 94304, USA; (M.J.C.); (R.T.M.); (H.S.); (J.R.); (A.D.)
2. Geisel School of Medicine at Dartmouth College, Hanover, NH 03755, USA
3. Department of Clinical Neurosciences, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
4. Department of Neurosurgery, Medical University of Graz, 8010 Graz, Austria
5. Department of Neurosurgery, University of Zurich, 8091 Zurich, Switzerland
6. Department of Neurosurgery, Santa Clara Valley Medical Center, 751 S Bascom Avenue, San Jose, CA 95128, USA
7. Faculty of Medicine, University of Oslo, 0316 Oslo, Norway

Abstract

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/awsaf49, at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The data presented in this study are openly available in BraTS2020 at https://www.kaggle.com/datasets/awsaf49/brats2020-training-data (brats2020-training-data) (accessed on 17 October 2025)

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 7 keywords, 33 references.

Cite

This paper

Sanker, V., Mahesh, D., Li, Z., Thaller, A., Heesen, P., Liverani, L., Wang, D., Cavagnaro, M. J., Medikonda, R. T., Prolo, L., Singh, H., Ratliff, J., & Desai, A. (2026). 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset. Journal of imaging, 12(6), 251. https://doi.org/10.3390/jimaging12060251

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