Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study
Abstract Background Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-support ML model for classifying patients wi...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | Journal of the Egyptian National Cancer Institute |
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| Online Access: | https://doi.org/10.1186/s43046-025-00265-3 |
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| author | Mostafa A. Arafa Karim H. Farhat Sherin F. Aly Farrukh K. Khan Alaa Mokhtar Abdulaziz M. Althunayan Waleed Al-Taweel Sultan S. Al-Khateeb Sami Azhari Danny M. Rabah |
| author_facet | Mostafa A. Arafa Karim H. Farhat Sherin F. Aly Farrukh K. Khan Alaa Mokhtar Abdulaziz M. Althunayan Waleed Al-Taweel Sultan S. Al-Khateeb Sami Azhari Danny M. Rabah |
| author_sort | Mostafa A. Arafa |
| collection | DOAJ |
| description | Abstract Background Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-support ML model for classifying patients with biopsy-negative (cancer-free), clinically significant, and non-clinically significant prostate cancer across two prostate-specific antigen (PSA) cut-offs ≤ 10 ng/ml and > 10 ng/ml. Methods The data for the current study were retrieved from the records of two main hospitals in Riyadh, Saudi Arabia from July 2018 through July 2024. Six machine learning algorithms were employed, and the dataset was randomly divided into a training set and a validation set at a ratio of 8:2. The following metrics were used as performance indicators across the six algorithms: Accuracy, Precision, Recall, F1-score, and area under the curve. Recent data from the two hospitals was utilized for external validation. Results The metrics for Random Forest, Extra Tree, and Decision Tree algorithms showed excellent capability in classifying the outcomes of prostate biopsy for the two PSA cut-offs. However, the metrics for the PSA cut-off > 10 ng/ml are higher than those for PSA ≤ 10 ng/ml. For the three-class classification, the accuracy and area under the curve for the cut-off > 10 ng/ml were 0.96 and 0.99, respectively. While for the cut-off ≤ 10 ng/ml they were 0.92 and 0.94 for Random Forest and 0.94 and 0.95 for the Extra Tree algorithm. The metrics of non-clinically significant and biopsy-negative cases outperformed those of clinically significant cases. Conclusion ML models are proving to be effective tools in differentiating between prostate biopsy outcomes, enhancing diagnostic accuracy, and potentially transforming clinical practices in prostate cancer management. |
| format | Article |
| id | doaj-art-775f5c93b26d4e4fb9b93bef245cc1cc |
| institution | Kabale University |
| issn | 2589-0409 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of the Egyptian National Cancer Institute |
| spelling | doaj-art-775f5c93b26d4e4fb9b93bef245cc1cc2025-08-20T03:41:47ZengSpringerOpenJournal of the Egyptian National Cancer Institute2589-04092025-03-013711810.1186/s43046-025-00265-3Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter studyMostafa A. Arafa0Karim H. Farhat1Sherin F. Aly2Farrukh K. Khan3Alaa Mokhtar4Abdulaziz M. Althunayan5Waleed Al-Taweel6Sultan S. Al-Khateeb7Sami Azhari8Danny M. Rabah9The Cancer Research Chair, Surgery Department, College of Medicine, King Saud UniversityThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud UniversityInformation Technology Department, Institute of Graduate Studies and Research, Alexandria UniversityThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud UniversityDepartment of Urology, King Faisal Specialist Hospital and Research CenterThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud UniversityDepartment of Urology, King Faisal Specialist Hospital and Research CenterDepartment of Urology, King Faisal Specialist Hospital and Research CenterCollege of Medicine, Alfaisal UniversityThe Cancer Research Chair, Surgery Department, College of Medicine, King Saud UniversityAbstract Background Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-support ML model for classifying patients with biopsy-negative (cancer-free), clinically significant, and non-clinically significant prostate cancer across two prostate-specific antigen (PSA) cut-offs ≤ 10 ng/ml and > 10 ng/ml. Methods The data for the current study were retrieved from the records of two main hospitals in Riyadh, Saudi Arabia from July 2018 through July 2024. Six machine learning algorithms were employed, and the dataset was randomly divided into a training set and a validation set at a ratio of 8:2. The following metrics were used as performance indicators across the six algorithms: Accuracy, Precision, Recall, F1-score, and area under the curve. Recent data from the two hospitals was utilized for external validation. Results The metrics for Random Forest, Extra Tree, and Decision Tree algorithms showed excellent capability in classifying the outcomes of prostate biopsy for the two PSA cut-offs. However, the metrics for the PSA cut-off > 10 ng/ml are higher than those for PSA ≤ 10 ng/ml. For the three-class classification, the accuracy and area under the curve for the cut-off > 10 ng/ml were 0.96 and 0.99, respectively. While for the cut-off ≤ 10 ng/ml they were 0.92 and 0.94 for Random Forest and 0.94 and 0.95 for the Extra Tree algorithm. The metrics of non-clinically significant and biopsy-negative cases outperformed those of clinically significant cases. Conclusion ML models are proving to be effective tools in differentiating between prostate biopsy outcomes, enhancing diagnostic accuracy, and potentially transforming clinical practices in prostate cancer management.https://doi.org/10.1186/s43046-025-00265-3Machine learning prediction modelsProstate biopsy outcomesProstate-specific antigen cut-offs |
| spellingShingle | Mostafa A. Arafa Karim H. Farhat Sherin F. Aly Farrukh K. Khan Alaa Mokhtar Abdulaziz M. Althunayan Waleed Al-Taweel Sultan S. Al-Khateeb Sami Azhari Danny M. Rabah Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study Journal of the Egyptian National Cancer Institute Machine learning prediction models Prostate biopsy outcomes Prostate-specific antigen cut-offs |
| title | Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study |
| title_full | Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study |
| title_fullStr | Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study |
| title_full_unstemmed | Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study |
| title_short | Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study |
| title_sort | prediction of prostate biopsy outcomes at different cut offs of prostate specific antigen using machine learning a multicenter study |
| topic | Machine learning prediction models Prostate biopsy outcomes Prostate-specific antigen cut-offs |
| url | https://doi.org/10.1186/s43046-025-00265-3 |
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