AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer
Abstract Biochemical recurrence (BCR) of prostate cancer (PCa) negatively impacts patients’ post-surgery quality of life, and the traditional predictive models have shown limited accuracy. This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) r...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88199-7 |
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| _version_ | 1832571881180364800 |
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| author | Jiawei Zhang Feng Ding Yitian Guo Xiaoying Wei Jibo Jing Feng Xu Huixing Chen Zhongying Guo Zonghao You Baotai Liang Ming Chen Dongfang Jiang Xiaobing Niu Xiangxue Wang Yifeng Xue |
| author_facet | Jiawei Zhang Feng Ding Yitian Guo Xiaoying Wei Jibo Jing Feng Xu Huixing Chen Zhongying Guo Zonghao You Baotai Liang Ming Chen Dongfang Jiang Xiaobing Niu Xiangxue Wang Yifeng Xue |
| author_sort | Jiawei Zhang |
| collection | DOAJ |
| description | Abstract Biochemical recurrence (BCR) of prostate cancer (PCa) negatively impacts patients’ post-surgery quality of life, and the traditional predictive models have shown limited accuracy. This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). Data from 545 patients across two centres are used for training and validation. The model showed strong performances, with high accuracy in identifying regions with high AR expression and BCR prediction. This AI model may help identify high-risk patients, aiding in better treatment strategies, particularly in underdeveloped areas. |
| format | Article |
| id | doaj-art-b2a854b598ba4a0da14d615c52d804d3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b2a854b598ba4a0da14d615c52d804d32025-02-02T12:16:05ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-88199-7AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancerJiawei Zhang0Feng Ding1Yitian Guo2Xiaoying Wei3Jibo Jing4Feng Xu5Huixing Chen6Zhongying Guo7Zonghao You8Baotai Liang9Ming Chen10Dongfang Jiang11Xiaobing Niu12Xiangxue Wang13Yifeng Xue14Department of Urology, Zhongda Hospital, Southeast UniversityNanjing University of Information Science and TechnologyDepartment of Urology, Zhongda Hospital, Southeast UniversityDepartment of Medical College, Southeast UniversityDepartment of Urology, Peking Union Medical College HospitalJinhu County People’s HospitalShanghai General Hospital, Urologic Medical Center, Shanghai Jiao Tong University School of MedicineDepartment of Pathology, Huaian First People’s HospitalDepartment of Urology, Zhongda Hospital, Southeast UniversityDepartment of Urology, Zhongda Hospital, Southeast UniversityDepartment of Urology, Zhongda Hospital, Southeast UniversityDepartment of Urology, The People’s Hospital of DanyangDepartment of Urology, Huaian First People’s HospitalNanjing University of Information Science and TechnologyThe Affiliated Jintan Hospital of Jiangsu UniversityAbstract Biochemical recurrence (BCR) of prostate cancer (PCa) negatively impacts patients’ post-surgery quality of life, and the traditional predictive models have shown limited accuracy. This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). Data from 545 patients across two centres are used for training and validation. The model showed strong performances, with high accuracy in identifying regions with high AR expression and BCR prediction. This AI model may help identify high-risk patients, aiding in better treatment strategies, particularly in underdeveloped areas.https://doi.org/10.1038/s41598-025-88199-7 |
| spellingShingle | Jiawei Zhang Feng Ding Yitian Guo Xiaoying Wei Jibo Jing Feng Xu Huixing Chen Zhongying Guo Zonghao You Baotai Liang Ming Chen Dongfang Jiang Xiaobing Niu Xiangxue Wang Yifeng Xue AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer Scientific Reports |
| title | AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| title_full | AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| title_fullStr | AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| title_full_unstemmed | AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| title_short | AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| title_sort | ai based prediction of androgen receptor expression and its prognostic significance in prostate cancer |
| url | https://doi.org/10.1038/s41598-025-88199-7 |
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