Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level
10.22465/kjuo.2019.17.2.110
- Author:
Jaegeun LEE
1
;
Seung Woo YANG
;
Seunghee LEE
;
Yun Kyong HYON
;
Jinbum KIM
;
Long JIN
;
Ji Yong LEE
;
Jong Mok PARK
;
Taeyoung HA
;
Ju Hyun SHIN
;
Jae Sung LIM
;
Yong Gil NA
;
Ki Hak SONG
Author Information
1. Department of Urology, Chungnam National University College of Medicine, Daejeon, Korea. urosong@cnu.ac.kr
- Publication Type:Original Article
- Keywords:
Prediction;
Prostate cancer;
Machine learning;
Prostate biopsy
- MeSH:
Biopsy;
Chungcheongnam-do;
Digital Rectal Examination;
Forests;
Humans;
Logistic Models;
Machine Learning;
Medical Records;
Prostate;
Prostate-Specific Antigen;
Prostatic Neoplasms;
Retrospective Studies;
Support Vector Machine;
Ultrasonography
- From:Korean Journal of Urological Oncology
2019;17(2):110-117
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND METHODS: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. RESULTS: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. CONCLUSIONS: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.
