A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach
Abstract Introduction Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two‐stage model designed for precise Gleason grading. The mod...
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| Format: | Article |
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Wiley
2025-03-01
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| Series: | Journal of Medical Radiation Sciences |
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| Online Access: | https://doi.org/10.1002/jmrs.841 |
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| author | Yuyan Zou Xuechun Wang Fen Ma Xulun Liu Chunyue Jiao Zhen Kang Jingjing Cui Yang Zhang Yan Xie Lei Chen Ronghua Tian |
| author_facet | Yuyan Zou Xuechun Wang Fen Ma Xulun Liu Chunyue Jiao Zhen Kang Jingjing Cui Yang Zhang Yan Xie Lei Chen Ronghua Tian |
| author_sort | Yuyan Zou |
| collection | DOAJ |
| description | Abstract Introduction Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two‐stage model designed for precise Gleason grading. The model initially uses radiomics‐based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features. Methods We retrospectively analysed 399 patients with PI‐RADS ≥ 3 lesions, categorising them into non‐significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2‐weighted and apparent diffusion coefficient (ADC) images, followed by the extraction of radiomics features. A two‐stage model was developed: the first stage identifies csPCa using radiomics‐based MRI, and the second integrates clinical indicators for Gleason grading. Model efficacy was evaluated by sensitivity, specificity, accuracy and area under the curve (AUC), with external validation on 100 patients. Results The first‐stage model demonstrated excellent diagnostic accuracy for csPCa, achieving AUCs of 0.989, 0.982 and 0.976 in the training, testing and external validation cohorts, respectively. The second‐stage model exhibited commendable Gleason grading capabilities, with AUCs of 0.82, 0.844 and 0.83 across the same cohorts. Decision curve analysis supported the clinical applicability of both models. Conclusions This study validated the potential of T2W and ADC image radiomics features as biomarkers in distinguishing csPCa. Combining these features with clinical indicators for csPCa Gleason grading provides superior predictive performance and significant clinical benefit. |
| format | Article |
| id | doaj-art-a0e6e10032b041b6bfefa21cd8ce56ed |
| institution | DOAJ |
| issn | 2051-3895 2051-3909 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Medical Radiation Sciences |
| spelling | doaj-art-a0e6e10032b041b6bfefa21cd8ce56ed2025-08-20T02:57:44ZengWileyJournal of Medical Radiation Sciences2051-38952051-39092025-03-017219310510.1002/jmrs.841A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approachYuyan Zou0Xuechun Wang1Fen Ma2Xulun Liu3Chunyue Jiao4Zhen Kang5Jingjing Cui6Yang Zhang7Yan Xie8Lei Chen9Ronghua Tian10Department of Radiology Xiaogan Hospital Affiliated to Wuhan University of Science and Technology Xiaogan ChinaDepartment of Research and Development Shanghai United Imaging Intelligence Co., Ltd. Shanghai ChinaDepartment of Radiology Xiaogan Hospital Affiliated to Wuhan University of Science and Technology Xiaogan ChinaAffiliated Hospital of Jiujiang University Jiujiang ChinaDepartment of Maternal and Child Health Care Yuyao City Hospital Yuyao ChinaDepartment of Radiology, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan ChinaDepartment of Research and Development United Imaging Intelligence (Beijing) Co., Ltd. Beijing ChinaDepartment of Research and Development Shanghai United Imaging Intelligence Co., Ltd. Shanghai ChinaDepartment of Research and Development Shanghai United Imaging Intelligence Co., Ltd. Shanghai ChinaDepartment of Research and Development Shanghai United Imaging Intelligence Co., Ltd. Shanghai ChinaDepartment of Radiology Xiaogan Hospital Affiliated to Wuhan University of Science and Technology Xiaogan ChinaAbstract Introduction Accurate identification and grading of clinically significant prostate cancer (csPCa, Gleason Score ≥ 7) without invasive procedures remains a significant clinical challenge. This study aims to develop and evaluate a two‐stage model designed for precise Gleason grading. The model initially uses radiomics‐based multiparametric MRI to identify csPCa and then refines the Gleason grading by integrating clinical indicators and radiomics features. Methods We retrospectively analysed 399 patients with PI‐RADS ≥ 3 lesions, categorising them into non‐significant prostate cancer (nsPCa, 263 cases) and csPCa (136 cases, subdivided by GGs). Regions of interest (ROIs) for the prostate and lesions were manually delineated on T2‐weighted and apparent diffusion coefficient (ADC) images, followed by the extraction of radiomics features. A two‐stage model was developed: the first stage identifies csPCa using radiomics‐based MRI, and the second integrates clinical indicators for Gleason grading. Model efficacy was evaluated by sensitivity, specificity, accuracy and area under the curve (AUC), with external validation on 100 patients. Results The first‐stage model demonstrated excellent diagnostic accuracy for csPCa, achieving AUCs of 0.989, 0.982 and 0.976 in the training, testing and external validation cohorts, respectively. The second‐stage model exhibited commendable Gleason grading capabilities, with AUCs of 0.82, 0.844 and 0.83 across the same cohorts. Decision curve analysis supported the clinical applicability of both models. Conclusions This study validated the potential of T2W and ADC image radiomics features as biomarkers in distinguishing csPCa. Combining these features with clinical indicators for csPCa Gleason grading provides superior predictive performance and significant clinical benefit.https://doi.org/10.1002/jmrs.841Clinically significant prostate cancer (csPCa)Gleason gradingmultiparametric MRI (mpMRI)prostate cancer (PCa)radiomics |
| spellingShingle | Yuyan Zou Xuechun Wang Fen Ma Xulun Liu Chunyue Jiao Zhen Kang Jingjing Cui Yang Zhang Yan Xie Lei Chen Ronghua Tian A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach Journal of Medical Radiation Sciences Clinically significant prostate cancer (csPCa) Gleason grading multiparametric MRI (mpMRI) prostate cancer (PCa) radiomics |
| title | A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach |
| title_full | A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach |
| title_fullStr | A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach |
| title_full_unstemmed | A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach |
| title_short | A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach |
| title_sort | two stage model for precise identification and gleason grading of clinically significant prostate cancer a hybrid approach |
| topic | Clinically significant prostate cancer (csPCa) Gleason grading multiparametric MRI (mpMRI) prostate cancer (PCa) radiomics |
| url | https://doi.org/10.1002/jmrs.841 |
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