A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images
Abstract This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00811-1 |
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| _version_ | 1832586046666178560 |
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| author | Chaoyue Chen Yanjie Zhao Linrui Cai Haoze Jiang Yuen Teng Yang Zhang Shuangyi Zhang Junkai Zheng Fumin Zhao Zhouyang Huang Xiaolong Xu Xin Zan Jianfeng Xu Lei Zhang Jianguo Xu |
| author_facet | Chaoyue Chen Yanjie Zhao Linrui Cai Haoze Jiang Yuen Teng Yang Zhang Shuangyi Zhang Junkai Zheng Fumin Zhao Zhouyang Huang Xiaolong Xu Xin Zan Jianfeng Xu Lei Zhang Jianguo Xu |
| author_sort | Chaoyue Chen |
| collection | DOAJ |
| description | Abstract This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods. Furthermore, Kaplan–Meier survival analysis was conducted to investigate whether the model could be used for tumor growth prediction. The model achieved superior results, with areas under the curve (AUCs) of 0.797 for internal testing and 0.808 for generalization, alongside 0.756 and 0.727 for 3- and 5-year tumor growth predictions, respectively. The prediction was significantly associated with the growth of asymptomatic small meningiomas. Overall, the model provides an effective tool for early prediction of Ki-67 and tumor volume growth, aiding in individualized patient management. |
| format | Article |
| id | doaj-art-3a5106a5c8cb440ba5c7a8ca39eadbcf |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-3a5106a5c8cb440ba5c7a8ca39eadbcf2025-01-26T12:12:54ZengNature Portfolionpj Precision Oncology2397-768X2025-01-01911910.1038/s41698-025-00811-1A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR imagesChaoyue Chen0Yanjie Zhao1Linrui Cai2Haoze Jiang3Yuen Teng4Yang Zhang5Shuangyi Zhang6Junkai Zheng7Fumin Zhao8Zhouyang Huang9Xiaolong Xu10Xin Zan11Jianfeng Xu12Lei Zhang13Jianguo Xu14Department of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDiseases of Women and Children, Sichuan University, Ministry of Education, No. 20, section 3, Renmin South Road, Wuhou DistrictDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Radiology, West China Second University Hospital, Sichuan University, No. 20, section 3, Renmin South Road, Wuhou DistrictDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyCollege of Computer Science, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyDepartment of Neurosurgery, Third People’s Hospital of Mianyang/Sichuan Mental Health Center, No. 190, East Section of Jiannan RoadCollege of Computer Science, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan University, West China Hosptial, No. 37, GuoXue AlleyAbstract This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods. Furthermore, Kaplan–Meier survival analysis was conducted to investigate whether the model could be used for tumor growth prediction. The model achieved superior results, with areas under the curve (AUCs) of 0.797 for internal testing and 0.808 for generalization, alongside 0.756 and 0.727 for 3- and 5-year tumor growth predictions, respectively. The prediction was significantly associated with the growth of asymptomatic small meningiomas. Overall, the model provides an effective tool for early prediction of Ki-67 and tumor volume growth, aiding in individualized patient management.https://doi.org/10.1038/s41698-025-00811-1 |
| spellingShingle | Chaoyue Chen Yanjie Zhao Linrui Cai Haoze Jiang Yuen Teng Yang Zhang Shuangyi Zhang Junkai Zheng Fumin Zhao Zhouyang Huang Xiaolong Xu Xin Zan Jianfeng Xu Lei Zhang Jianguo Xu A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images npj Precision Oncology |
| title | A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images |
| title_full | A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images |
| title_fullStr | A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images |
| title_full_unstemmed | A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images |
| title_short | A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images |
| title_sort | multi modal deep learning model for prediction of ki 67 for meningiomas using pretreatment mr images |
| url | https://doi.org/10.1038/s41698-025-00811-1 |
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