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A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration

MCML Authors

Link to Profile Benedikt Wiestler PI Matchmaking

Benedikt Wiestler

Prof. Dr.

Principal Investigator

AI for Image-Guided Diagnosis and Therapy

Abstract

Glioblastoma, the most aggressive primary brain tumor, poses a severe clinical challenge due to its diffuse microscopic infiltration, which remains largely undetected on standard MRI. As a result, current radiotherapy planning employs a uniform 15 mm margin around the resection cavity, failing to capture patient-specific tumor spread. Tumor growth modeling offers a promising approach to reveal this hidden infiltration. However, methods based on partial differential equations or physics-informed neural networks tend to be computationally intensive or overly constrained, limiting their clinical adaptability to individual patients. In this work, we propose a lightweight, rapid, and robust optimization framework that estimates the 3D tumor concentration by fitting it to MRI tumor segmentations while enforcing a smooth concentration landscape. This approach achieves superior tumor recurrence prediction on 192 brain tumor patients across two public datasets, outperforming state-of-the-art baselines while reducing runtime from 30 minutes to less than one minute. Furthermore, we demonstrate the framework's versatility and adaptability by showing its ability to seamlessly integrate additional imaging modalities or physical constraints.

inproceedings

CMMCA @MICCAI 2025

Workshop on Computational Mathematics Modeling in Cancer Analysis at 28th International Conference on Medical Image Computing and Computer Assisted Intervention. Daejeon, Republic of Korea, Sep 23-27, 2025.

Authors

J. Weidner • M. Balcerak • I. Ezhov • A. Datchev • L. Lux • L. Zimmer • D. Rückert • B. Menze • B. Wiestler

Links

DOI

Research Area

 C1 | Medicine

BibTeXKey: WBE+25

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