# Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss

Source: [Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss](<https://www.sota2.com/research/paper/68fa46cf1e209131ce923477>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-07T05:01:26.657Z

Authors: Michal Balcerak, Jonas Weidner, Petr Karnakov, Ivan Ezhov, Sergey Litvinov, Petros Koumoutsakos, Tamaz Amiranashvili, Ray Zirui Zhang, John S. Lowengrub, Bene Wiestler, Bjoern Menze

Published: 2023-12-08T14:32:14.000Z

## Abstract

Brain tumor growth is unique to each glioma patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. Understanding these hidden patient-specific progressions is essential for effective therapies. Current treatment plans for brain tumors, such as radiotherapy, typically involve delineating a uniform margin around the visible tumor on pre-treatment scans to target this invisible tumor growth. This "one size fits all" approach is derived from population studies and often fails to account for the nuances of individual patient conditions. We present the GliODIL framework, which infers the full spatial distribution of tumor cell concentration from available multi-modal imaging, leveraging a Fisher-Kolmogorov type physics model to describe tumor growth. This is achieved through the newly introduced method of Optimizing the Discrete Loss, where both data and physics-based constraints are softly assimilated into the solution. Our test dataset comprises 152 glioblastoma patients with pre-treatment imaging and post-treatment follow-ups for tumor recurrence monitoring. By blending data-driven techniques with physics-based constraints, GliODIL enhances recurrence prediction in radiotherapy planning, challenging traditional uniform margins and strict adherence to the Fisher-Kolmogorov partial differential equation model, which is adapted for complex cases.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Recurrence Segmentation Coverage Prediction on 58-patient MRI and FET-PET dataset (test)](<https://www.sota2.com/research/sota/recurrence-segmentation-coverage-prediction-on-58-patient-mri-and-fet-pet-dataset-test.md>): Recurrence Segmentation Coverage Prediction; 58-patient MRI and FET-PET dataset (test); Recurrence Coverage (Any): 72.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.05063>)

[PDF](<https://arxiv.org/pdf/2312.05063.pdf>)