Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss
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 ra...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60366-4 |
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| author | Michal Balcerak Jonas Weidner Petr Karnakov Ivan Ezhov Sergey Litvinov Petros Koumoutsakos Tamaz Amiranashvili Ray Zirui Zhang John S. Lowengrub Igor Yakushev Benedikt Wiestler Bjoern Menze |
| author_facet | Michal Balcerak Jonas Weidner Petr Karnakov Ivan Ezhov Sergey Litvinov Petros Koumoutsakos Tamaz Amiranashvili Ray Zirui Zhang John S. Lowengrub Igor Yakushev Benedikt Wiestler Bjoern Menze |
| author_sort | Michal Balcerak |
| collection | DOAJ |
| description | 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 Glioma Optimizing the Discrete Loss (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 (ODIL), 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 | Article |
| id | doaj-art-a32b60ab9c474ce095731de35455474d |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a32b60ab9c474ce095731de35455474d2025-08-20T03:05:06ZengNature PortfolioNature Communications2041-17232025-07-0116111610.1038/s41467-025-60366-4Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete lossMichal Balcerak0Jonas Weidner1Petr Karnakov2Ivan Ezhov3Sergey Litvinov4Petros Koumoutsakos5Tamaz Amiranashvili6Ray Zirui Zhang7John S. Lowengrub8Igor Yakushev9Benedikt Wiestler10Bjoern Menze11Department of Quantitative Biomedicine, University of ZurichDepartment of Computer Science, Technical University of MunichComputational Science and Engineering Laboratory, Harvard UniversityDepartment of Computer Science, Technical University of MunichComputational Science and Engineering Laboratory, Harvard UniversityComputational Science and Engineering Laboratory, Harvard UniversityDepartment of Quantitative Biomedicine, University of ZurichDepartment of Mathematics, University of CaliforniaDepartment of Mathematics, University of CaliforniaDepartment of Nuclear Medicine, Technical University of MunichMunich Center for Machine Learning (MCML)Department of Quantitative Biomedicine, University of ZurichAbstract 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 Glioma Optimizing the Discrete Loss (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 (ODIL), 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.https://doi.org/10.1038/s41467-025-60366-4 |
| spellingShingle | Michal Balcerak Jonas Weidner Petr Karnakov Ivan Ezhov Sergey Litvinov Petros Koumoutsakos Tamaz Amiranashvili Ray Zirui Zhang John S. Lowengrub Igor Yakushev Benedikt Wiestler Bjoern Menze Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss Nature Communications |
| title | Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss |
| title_full | Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss |
| title_fullStr | Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss |
| title_full_unstemmed | Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss |
| title_short | Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss |
| title_sort | individualizing glioma radiotherapy planning by optimization of a data and physics informed discrete loss |
| url | https://doi.org/10.1038/s41467-025-60366-4 |
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