A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration

TL;DR

This paper introduces a fast, lightweight optimization framework for estimating 3D brain tumor infiltration from MRI data, significantly improving prediction accuracy and runtime efficiency over existing methods.

Contribution

It presents a novel, computationally efficient framework that estimates tumor infiltration, outperforming state-of-the-art methods and adaptable to various imaging modalities and constraints.

Findings

Outperforms existing methods in tumor recurrence prediction

Reduces runtime from 30 minutes to under one minute

Demonstrates versatility with additional imaging modalities

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.