PMID- 38537959
OWN - NLM
STAT- MEDLINE
DCOM- 20240329
LR  - 20240329
IS  - 1791-7530 (Electronic)
IS  - 0250-7005 (Linking)
VI  - 44
IP  - 4
DP  - 2024 Apr
TI  - Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate 
      Cancer in A Benign Prostate Hyperplasia Cohort.
PG  - 1683-1693
LID - 10.21873/anticanres.16967 [doi]
AB  - 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.
CI  - Copyright (c) 2024 International Institute of Anticancer Research (Dr. George J. 
      Delinasios), All rights reserved.
FAU - Chang, Chia-Cheng
AU  - Chang CC
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
FAU - Chiou, Jiun-Kai
AU  - Chiou JK
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
FAU - Lin, Cheng-Jian
AU  - Lin CJ
AD  - Department of Computer Science & Information Engineering, National Chin-Yi 
      University of Technology, Taichung, Taiwan, R.O.C.
FAU - Lu, Kevin
AU  - Lu K
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.; kevinlu0620@mail2000.com.tw.
AD  - School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan, 
      R.O.C.
FAU - Li, Jian-Ri
AU  - Li JR
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
FAU - Chang, Li-Wen
AU  - Chang LW
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
FAU - Hung, Sheng-Chun
AU  - Hung SC
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
FAU - Cheng, Chen-Li
AU  - Cheng CL
AD  - Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan, 
      R.O.C.
AD  - Department of Computer Science & Information Engineering, National Chin-Yi 
      University of Technology, Taichung, Taiwan, R.O.C.
LA  - eng
PT  - Journal Article
PL  - Greece
TA  - Anticancer Res
JT  - Anticancer research
JID - 8102988
RN  - EC 3.4.21.77 (Prostate-Specific Antigen)
RN  - EC 1.- (Oxidoreductases)
SB  - IM
MH  - Male
MH  - Humans
MH  - Prostate
MH  - Prostate-Specific Antigen
MH  - *Prostatic Hyperplasia/diagnosis/complications
MH  - Retrospective Studies
MH  - Hyperplasia
MH  - Early Detection of Cancer
MH  - *Prostatic Neoplasms/diagnosis/complications
MH  - Algorithms
MH  - Machine Learning
MH  - Oxidoreductases
OTO - NOTNLM
OT  - KNN
OT  - Machine learning
OT  - SVM
OT  - XGB
OT  - benign prostatic hyperplasia
OT  - modeling
OT  - prostate cancer risk
EDAT- 2024/03/28 00:45
MHDA- 2024/03/29 06:45
CRDT- 2024/03/27 20:43
PHST- 2023/07/23 00:00 [received]
PHST- 2023/12/30 00:00 [revised]
PHST- 2024/01/30 00:00 [accepted]
PHST- 2024/03/29 06:45 [medline]
PHST- 2024/03/28 00:45 [pubmed]
PHST- 2024/03/27 20:43 [entrez]
AID - 44/4/1683 [pii]
AID - 10.21873/anticanres.16967 [doi]
PST - ppublish
SO  - Anticancer Res. 2024 Apr;44(4):1683-1693. doi: 10.21873/anticanres.16967.