دورية أكاديمية
Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate Cancer in A Benign Prostate Hyperplasia Cohort.
| العنوان: | Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate Cancer in A Benign Prostate Hyperplasia Cohort. |
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| المؤلفون: | Chang CC; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C., Chiou JK; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C., Lin CJ; Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C., Lu K; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.; kevinlu0620@mail2000.com.tw.; School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan, R.O.C., Li JR; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C., Chang LW; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C., Hung SC; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C., Cheng CL; Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.; Department of Computer Science & Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan, R.O.C. |
| المصدر: | Anticancer research [Anticancer Res] 2024 Apr; Vol. 44 (4), pp. 1683-1693. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: International Institute of Anticancer Research Country of Publication: Greece NLM ID: 8102988 Publication Model: Print Cited Medium: Internet ISSN: 1791-7530 (Electronic) Linking ISSN: 02507005 NLM ISO Abbreviation: Anticancer Res Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Attiki, Greece : International Institute of Anticancer Research Original Publication: Athens, Greece : Potamitis Press |
| مواضيع طبية MeSH: | Prostatic Hyperplasia*/diagnosis , Prostatic Hyperplasia*/complications , Prostatic Neoplasms*/diagnosis , Prostatic Neoplasms*/complications, Male ; Humans ; Prostate ; Prostate-Specific Antigen ; Retrospective Studies ; Hyperplasia ; Early Detection of Cancer ; Algorithms ; Machine Learning ; Oxidoreductases |
| مستخلص: | Background/aim: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance. Patients and Methods: The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled. Patients were excluded if they had a previous diagnosis of any cancer or were diagnosed with PCa within 1 month of enrolment. The study endpoint was PCa diagnosis. The study utilized the extreme gradient boosting (XGB), support vector machine (SVM) and K-nearest neighbors (KNN) machine-learning algorithms for analysis. Results: The dataset used in this study included 5,122 medical records of patients with and without PCa, with 19 patient characteristics. The SVM and XGB models performed better than the KNN model in terms of accuracy and area under curve. Local interpretable model-agnostic explanation and Shapley additive explanations analysis showed that body mass index (BMI) and late prostate-specific antigen (PSA) were important features for the SVM model, while PSA velocity, late PSA, and BMI were important features for the XGB model. Use of 5-alpha-reductase inhibitor was associated with a higher incidence of PCa, with similar survival outcomes compared to non-users. Conclusion: Machine learning can enhance personalized PCa risk assessments for patients with BPH but more research is necessary to refine these models and address data biases. Clinicians should use them as supplementary tools alongside traditional screening methods. (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.) |
| فهرسة مساهمة: | Keywords: KNN; Machine learning; SVM; XGB; benign prostatic hyperplasia; modeling; prostate cancer risk |
| المشرفين على المادة: | EC 3.4.21.77 (Prostate-Specific Antigen) EC 1.- (Oxidoreductases) |
| تواريخ الأحداث: | Date Created: 20240327 Date Completed: 20240329 Latest Revision: 20240329 |
| رمز التحديث: | 20240329 |
| DOI: | 10.21873/anticanres.16967 |
| PMID: | 38537959 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1791-7530 |
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| DOI: | 10.21873/anticanres.16967 |