# Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference

arXiv: https://arxiv.org/abs/1807.00499 · HTML: https://mlsanity.com/paper/1807.00499/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 25 of 25)

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- [Brain-WM: Brain Glioblastoma World Model](https://mlsanity.com/paper/2603.07562/) (2026)
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- [PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning](https://mlsanity.com/paper/2509.13360/) (2025)
- [TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth](https://mlsanity.com/paper/2603.04058/) (2026)
- [Initial condition assessment for reaction-diffusion glioma growth models: A translational MRI/histology (in)validation study](https://mlsanity.com/paper/2102.01719/) (2021)
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- [Predicting multiple sclerosis disease severity with multimodal deep neural networks](https://mlsanity.com/paper/2304.04062/) (2023)
- [A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration](https://mlsanity.com/paper/2412.13811/) (2024)
- [Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models](https://mlsanity.com/paper/2501.08226/) (2025)
- [Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss](https://mlsanity.com/paper/2510.15664/) (2025)
- [Evaluating glioma growth predictions as a forward ranking problem](https://mlsanity.com/paper/2103.11651/) (2021)
- [Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)](https://mlsanity.com/paper/2308.01318/) (2023)

## Abstract (arXiv)

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with confidence intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.