Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI
Abstract The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01861-2 |
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| author | Nate Tran Tracy L. Luks Yan Li Angela Jakary Jacob Ellison Bo Liu Oluwaseun Adegbite Devika Nair Pranav Kakhandiki Annette M. Molinaro Javier E. Villanueva-Meyer Nicholas Butowski Jennifer L. Clarke Susan M. Chang Steve E. Braunstein Olivier Morin Hui Lin Janine M. Lupo |
| author_facet | Nate Tran Tracy L. Luks Yan Li Angela Jakary Jacob Ellison Bo Liu Oluwaseun Adegbite Devika Nair Pranav Kakhandiki Annette M. Molinaro Javier E. Villanueva-Meyer Nicholas Butowski Jennifer L. Clarke Susan M. Chang Steve E. Braunstein Olivier Morin Hui Lin Janine M. Lupo |
| author_sort | Nate Tran |
| collection | DOAJ |
| description | Abstract The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs. |
| format | Article |
| id | doaj-art-b5abaec0a4e24f35912a3e464393975d |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b5abaec0a4e24f35912a3e464393975d2025-08-20T03:43:34ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111210.1038/s41746-025-01861-2Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRINate Tran0Tracy L. Luks1Yan Li2Angela Jakary3Jacob Ellison4Bo Liu5Oluwaseun Adegbite6Devika Nair7Pranav Kakhandiki8Annette M. Molinaro9Javier E. Villanueva-Meyer10Nicholas Butowski11Jennifer L. Clarke12Susan M. Chang13Steve E. Braunstein14Olivier Morin15Hui Lin16Janine M. Lupo17Department of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Neurological Surgery, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaDepartment of Neurological Surgery, University of CaliforniaDepartment of Neurological Surgery, University of CaliforniaDepartment of Neurological Surgery, University of CaliforniaDepartment of Radiation Oncology, University of CaliforniaDepartment of Radiation Oncology, University of CaliforniaUCSF/UC Berkeley Graduate Program in Bioengineering, University of CaliforniaDepartment of Radiology & Biomedical Imaging, University of CaliforniaAbstract The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.https://doi.org/10.1038/s41746-025-01861-2 |
| spellingShingle | Nate Tran Tracy L. Luks Yan Li Angela Jakary Jacob Ellison Bo Liu Oluwaseun Adegbite Devika Nair Pranav Kakhandiki Annette M. Molinaro Javier E. Villanueva-Meyer Nicholas Butowski Jennifer L. Clarke Susan M. Chang Steve E. Braunstein Olivier Morin Hui Lin Janine M. Lupo Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI npj Digital Medicine |
| title | Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI |
| title_full | Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI |
| title_fullStr | Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI |
| title_full_unstemmed | Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI |
| title_short | Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI |
| title_sort | novel radiotherapy target definition using ai driven predictions of glioblastoma recurrence from metabolic and diffusion mri |
| url | https://doi.org/10.1038/s41746-025-01861-2 |
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