Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study
Abstract To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82909-3 |
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| author | Lingkai Cai Xiao Yang Jie Yu Qiang Shao Gongcheng Wang Baorui Yuan Juntao Zhuang Kai Li Qikai Wu Peikun Liu Ruixi Yu Qiang Cao Pengchao Li Xueying Sun Yuan Zou Xue Fu Xiangming Fang Chunxiao Chen Qiang Lu |
| author_facet | Lingkai Cai Xiao Yang Jie Yu Qiang Shao Gongcheng Wang Baorui Yuan Juntao Zhuang Kai Li Qikai Wu Peikun Liu Ruixi Yu Qiang Cao Pengchao Li Xueying Sun Yuan Zou Xue Fu Xiangming Fang Chunxiao Chen Qiang Lu |
| author_sort | Lingkai Cai |
| collection | DOAJ |
| description | Abstract To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized to differentiate between MIBC and NMIBC based on three-channel image inputs, including original T2WI images, segmented bladder, and regions of interest. Inception V3 was employed for model construction. The accuracy, sensitivity (SN), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) for predicting MIBC by DL model were 92.4%, 94.7%, 91.5%, 81.8% and 97.7% in the validation set and 92.1%, 86.8%, 94.6%, 88.5% and 93.8% in the internal test set. In the external test set, these values were 81.6%, 57.1%, 87.1%, 50.0% and 90.0%. Additionally, the accuracy, SN, SP, PPV, and NPV for predicting MIBC were 93.5%, 100%, 93.4%, 11.1%, and 100% in VI-RADS 2; 80.0%, 66.7%, 87.2%, 73.7% and 82.9% in VI-RADS 3; 90.3%, 91.7%, 85.7%, 95.7%, 75.0% in VI-RADS 4. The accuracy, SN, and PPV were 93.9%, 93.9%, and 100% in VI-RADS 5. The DL model based on T2WI can effectively predict MIBC and serve as a valuable complement to VI-RADS 3. |
| format | Article |
| id | doaj-art-68e11aae5a334c06abea6f1e84c7ad4c |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-68e11aae5a334c06abea6f1e84c7ad4c2025-08-20T02:41:34ZengNature PortfolioScientific Reports2045-23222025-03-011511910.1038/s41598-024-82909-3Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical studyLingkai Cai0Xiao Yang1Jie Yu2Qiang Shao3Gongcheng Wang4Baorui Yuan5Juntao Zhuang6Kai Li7Qikai Wu8Peikun Liu9Ruixi Yu10Qiang Cao11Pengchao Li12Xueying Sun13Yuan Zou14Xue Fu15Xiangming Fang16Chunxiao Chen17Qiang Lu18Department of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Urology, Suzhou Hospital Affiliated of Nanjing Medical UniversityDepartment of Urology, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Radiology, Wuxi Medical Center of Nanjing Medical UniversityDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityAbstract To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized to differentiate between MIBC and NMIBC based on three-channel image inputs, including original T2WI images, segmented bladder, and regions of interest. Inception V3 was employed for model construction. The accuracy, sensitivity (SN), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) for predicting MIBC by DL model were 92.4%, 94.7%, 91.5%, 81.8% and 97.7% in the validation set and 92.1%, 86.8%, 94.6%, 88.5% and 93.8% in the internal test set. In the external test set, these values were 81.6%, 57.1%, 87.1%, 50.0% and 90.0%. Additionally, the accuracy, SN, SP, PPV, and NPV for predicting MIBC were 93.5%, 100%, 93.4%, 11.1%, and 100% in VI-RADS 2; 80.0%, 66.7%, 87.2%, 73.7% and 82.9% in VI-RADS 3; 90.3%, 91.7%, 85.7%, 95.7%, 75.0% in VI-RADS 4. The accuracy, SN, and PPV were 93.9%, 93.9%, and 100% in VI-RADS 5. The DL model based on T2WI can effectively predict MIBC and serve as a valuable complement to VI-RADS 3.https://doi.org/10.1038/s41598-024-82909-3Bladder cancerMRIMIBCDeep learning |
| spellingShingle | Lingkai Cai Xiao Yang Jie Yu Qiang Shao Gongcheng Wang Baorui Yuan Juntao Zhuang Kai Li Qikai Wu Peikun Liu Ruixi Yu Qiang Cao Pengchao Li Xueying Sun Yuan Zou Xue Fu Xiangming Fang Chunxiao Chen Qiang Lu Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study Scientific Reports Bladder cancer MRI MIBC Deep learning |
| title | Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study |
| title_full | Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study |
| title_fullStr | Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study |
| title_full_unstemmed | Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study |
| title_short | Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study |
| title_sort | deep learning on t2wi to predict the muscle invasive bladder cancer a multi center clinical study |
| topic | Bladder cancer MRI MIBC Deep learning |
| url | https://doi.org/10.1038/s41598-024-82909-3 |
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