Korean J Urol Oncol.  2019 Aug;17(2):110-117. 10.22465/kjuo.2019.17.2.110.

Machine Learning Approaches for the Prediction of Prostate Cancer according to Age and the Prostate-Specific Antigen Level

• Lee J
• Yang SW
• Lee S
• Hyon YK
• Kim J
• Jin L
• Lee JY
• Park JM
• Ha T
• Shin JH
• Lim JS
• Na YG
• Song KH

• KMID: 2457595
• DOI: http://doi.org/10.22465/kjuo.2019.17.2.110

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.

Keyword

Prediction; Prostate cancer; Machine learning; Prostate biopsy

MeSH Terms

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
Prostate-Specific Antigen