Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model
Abstract Bladder lesion commonly occurs in patients with benign prostatic hyperplasia (BPH), and the routine screening of bladder lesion is vital for its timely detection and treatment, in which the risk of bladder lesion progression can be effectively alleviated. However, current clinical methods a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-75104-x |
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| author | Yunxin Wang Jiachuang Li Yunfeng Song Hongguo Wei Zejun Yan Shuo Chen Zhe Zhang |
| author_facet | Yunxin Wang Jiachuang Li Yunfeng Song Hongguo Wei Zejun Yan Shuo Chen Zhe Zhang |
| author_sort | Yunxin Wang |
| collection | DOAJ |
| description | Abstract Bladder lesion commonly occurs in patients with benign prostatic hyperplasia (BPH), and the routine screening of bladder lesion is vital for its timely detection and treatment, in which the risk of bladder lesion progression can be effectively alleviated. However, current clinical methods are inconvenient for routine screening. In this study, we proposed a convenient routine screening method to diagnose bladder lesions based on several clinical risk factors, which can be obtained through non-invasive, easy-to-operate, and low-cost examinations. The contribution of each clinical risk factor was further quantitatively analyzed to understand their impact on diagnostic decision-making. Based on a cohort study of 253 BPH patients with or without bladder lesions, the proposed diagnostic model achieved high accuracy using these clinical risk factors. Bladder compliance, maximum flow rate (Qmax), prostate specific antigen (PSA), and postvoid residual (PVR) were identified as the four most important clinical risk factors. To the best of our knowledge, this is the innovative research to predict bladder lesions based on the risk factors and quantitatively reveal their contributions to diagnostic decision-making. The proposed model has the potential to serve as an effective routine screening tool for bladder lesions in BPH patients, enabling early intervention to prevent lesion progression and improve the quality of life. |
| format | Article |
| id | doaj-art-aed116a199ea414992ae4a8a398bf1be |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aed116a199ea414992ae4a8a398bf1be2025-08-20T02:17:47ZengNature PortfolioScientific Reports2045-23222024-10-0114111310.1038/s41598-024-75104-xInvestigation on clinical risk factors of bladder lesion by machine learning based interpretable modelYunxin Wang0Jiachuang Li1Yunfeng Song2Hongguo Wei3Zejun Yan4Shuo Chen5Zhe Zhang6College of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Urology, The First Affiliated Hospital of China Medical UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Urology, The First Affiliated Hospital of Ningbo UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Urology, The First Affiliated Hospital of China Medical UniversityAbstract Bladder lesion commonly occurs in patients with benign prostatic hyperplasia (BPH), and the routine screening of bladder lesion is vital for its timely detection and treatment, in which the risk of bladder lesion progression can be effectively alleviated. However, current clinical methods are inconvenient for routine screening. In this study, we proposed a convenient routine screening method to diagnose bladder lesions based on several clinical risk factors, which can be obtained through non-invasive, easy-to-operate, and low-cost examinations. The contribution of each clinical risk factor was further quantitatively analyzed to understand their impact on diagnostic decision-making. Based on a cohort study of 253 BPH patients with or without bladder lesions, the proposed diagnostic model achieved high accuracy using these clinical risk factors. Bladder compliance, maximum flow rate (Qmax), prostate specific antigen (PSA), and postvoid residual (PVR) were identified as the four most important clinical risk factors. To the best of our knowledge, this is the innovative research to predict bladder lesions based on the risk factors and quantitatively reveal their contributions to diagnostic decision-making. The proposed model has the potential to serve as an effective routine screening tool for bladder lesions in BPH patients, enabling early intervention to prevent lesion progression and improve the quality of life.https://doi.org/10.1038/s41598-024-75104-xBladder lesionClinical risk factorsRoutine screeningMachine learningInterpretable model |
| spellingShingle | Yunxin Wang Jiachuang Li Yunfeng Song Hongguo Wei Zejun Yan Shuo Chen Zhe Zhang Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model Scientific Reports Bladder lesion Clinical risk factors Routine screening Machine learning Interpretable model |
| title | Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| title_full | Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| title_fullStr | Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| title_full_unstemmed | Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| title_short | Investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| title_sort | investigation on clinical risk factors of bladder lesion by machine learning based interpretable model |
| topic | Bladder lesion Clinical risk factors Routine screening Machine learning Interpretable model |
| url | https://doi.org/10.1038/s41598-024-75104-x |
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