Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| author | Balcerak, Michal Weidner, Jonas Karnakov, Petr Ezhov, Ivan Litvinov, Sergey Koumoutsakos, Petros Amiranashvili, Tamaz Zhang, Ray Zirui Lowengrub, John S. Wiestler, Bene Menze, Bjoern |
| author_facet | Balcerak, Michal Weidner, Jonas Karnakov, Petr Ezhov, Ivan Litvinov, Sergey Koumoutsakos, Petros Amiranashvili, Tamaz Zhang, Ray Zirui Lowengrub, John S. Wiestler, Bene Menze, Bjoern |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05063 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss Balcerak, Michal Weidner, Jonas Karnakov, Petr Ezhov, Ivan Litvinov, Sergey Koumoutsakos, Petros Amiranashvili, Tamaz Zhang, Ray Zirui Lowengrub, John S. Wiestler, Bene Menze, Bjoern Medical Physics Numerical Analysis Quantitative Methods 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. |
| title | Individualizing Glioma Radiotherapy Planning by Optimization of Data and Physics-Informed Discrete Loss |
| topic | Medical Physics Numerical Analysis Quantitative Methods |
| url | https://arxiv.org/abs/2312.05063 |