Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images
Abstract Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaS...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01756-2 |
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| _version_ | 1850207179679727616 |
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| author | Yuhan Zhang Xiao Ma Mingchao Li Kun Huang Jie Zhu Miao Wang Xi Wang Menglin Wu Pheng-Ann Heng |
| author_facet | Yuhan Zhang Xiao Ma Mingchao Li Kun Huang Jie Zhu Miao Wang Xi Wang Menglin Wu Pheng-Ann Heng |
| author_sort | Yuhan Zhang |
| collection | DOAJ |
| description | Abstract Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3–8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios. |
| format | Article |
| id | doaj-art-e2983803337e4ba5b49e915fa8fb2e4c |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-e2983803337e4ba5b49e915fa8fb2e4c2025-08-20T02:10:35ZengNature Portfolionpj Digital Medicine2398-63522025-06-018111110.1038/s41746-025-01756-2Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI imagesYuhan Zhang0Xiao Ma1Mingchao Li2Kun Huang3Jie Zhu4Miao Wang5Xi Wang6Menglin Wu7Pheng-Ann Heng8School of Biomedical Engineering, Shenzhen UniversityDepartment of Computer Science and Engineering, Nanjing University of Science and TechnologyAcademy of Arts and Design, Tsinghua UniversityDepartment of Computer Science and Engineering, Nanjing University of Science and TechnologySenior Department of Urology, The Third Medical Center of Chinese PLA General HospitalDepartment of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical SciencesDepartment of Computer Science and Engineering, The Chinese University of Hong KongCarbon Medical Device Ltd.Department of Computer Science and Engineering, The Chinese University of Hong KongAbstract Prostate cancer (PCa) is one of the most common types of cancer, seriously affecting adult male health. Accurate and automated PCa segmentation is essential for radiologists to confirm the location of cancer, evaluate its severity, and design appropriate treatments. This paper presents PCaSAM, a fully automated PCa segmentation model that allows us to input multi-modal MRI images into the foundation model to improve performance significantly. We collected multi-center datasets to conduct a comprehensive evaluation. The results showed that PCaSAM outperforms the generalist medical foundation model and the other representative segmentation models, with the average DSC of 0.721 and 0.706 in the internal and external datasets, respectively. Furthermore, with the assistance of segmentation, the PI-RADS scoring of PCa lesions was improved significantly, leading to a substantial increase in average AUC by 8.3–8.9% on two external datasets. Besides, PCaSAM achieved superior efficiency, making it highly suitable for real-world deployment scenarios.https://doi.org/10.1038/s41746-025-01756-2 |
| spellingShingle | Yuhan Zhang Xiao Ma Mingchao Li Kun Huang Jie Zhu Miao Wang Xi Wang Menglin Wu Pheng-Ann Heng Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images npj Digital Medicine |
| title | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images |
| title_full | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images |
| title_fullStr | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images |
| title_full_unstemmed | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images |
| title_short | Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images |
| title_sort | generalist medical foundation model improves prostate cancer segmentation from multimodal mri images |
| url | https://doi.org/10.1038/s41746-025-01756-2 |
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