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

Vivek Sanker¹, Dhanya Mahesh², Zhikai Li³

¹Department of Neurosurgery, Stanford University, Stanford, CA 94304, USA.

Journal of Imaging

|June 25, 2026

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This summary is machine-generated.

This study developed a 3D deep learning framework for brain tumor segmentation and survival prediction using multi-modal MRI data. The framework shows promise for improving clinical practice through accurate segmentation and reliable survival forecasting.

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Oncology
Computational Neuroscience

Background:

Three-dimensional deep learning (3D DL) shows potential for automated brain tumor segmentation and survival prediction.
Robust validation across multiple MRI modalities is crucial for clinical implementation of 3D DL.

Purpose of the Study:

To develop and validate a comprehensive 3D deep learning framework for brain tumor segmentation and survival prediction.
To assess the performance of multi-modal MRI data in enhancing segmentation and classification accuracy.

Main Methods:

A 3D U-Net architecture was developed using 369 cases from the BraTS2020 dataset for tumor segmentation.
Segmentation outputs were used to train machine learning classifiers for 6-month and 12-month survival prediction.

Keywords:

3D deep learning brain tumor segmentation glioma medical image analysis neuro-oncology radiomics survival prediction

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Models were evaluated using Dice Similarity Coefficient, Hausdorff Distance, AUC-ROC, and balanced accuracy.

Main Results:

The 3D U-Net model achieved a mean validation Dice score of 0.8388 for segmentation.
Survival classification for 12-month prediction yielded an AUC of 0.746 with 69% accuracy.
Key predictors for 12-month survival included extent of resection and tumor medians from T1 contrast-enhanced and FLAIR MRI.

Conclusions:

A multi-modal 3D deep learning approach significantly enhances performance in brain tumor analysis.
Segmentation-derived features hold promise for accurate patient survival prediction.
The developed framework offers a robust tool for clinical application in neuro-oncology.