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

대한비뇨기종양학회지 2019년 17권 2호 p.110 ~ 117
 ( Lee Jae-Geun ) - Chungnam National University School of Medicine Department of Urology

 ( Yang Seung-Woo ) - Chungnam National University College of Medicine Department of Urology
 ( Lee Seung-Hee ) - National Institute for Mathematical Sciences Division of Medical Mathematics
 ( Hyon Yun-Kyong ) - National Institute for Mathematical Sciences Division of Medical Mathematics
 ( Kim Jin-Bum ) - Konyang University College of Medicine Department of Urology
 ( Jin Long ) - Chungnam National University College of Medicine Department of Urology
 ( Lee Ji-Yong ) - Chungnam National University College of Medicine Department of Urology
 ( Park Jong-Mok ) - Chungnam National University College of Medicine Department of Urology
 ( Ha Tae-Young ) - National Institute for Mathematical Sciences Division of Medical Mathematics
 ( Shin Ju-Hyun ) - Chungnam National University College of Medicine Department of Urology
 ( Lim Jae-Sung ) - Chungnam National University College of Medicine Department of Urology
 ( Na Yong-Gil ) - Chungnam National University College of Medicine Department of Urology
 ( Song Ki-Hak ) - Chungnam National University College of Medicine Department of Urology

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.

키워드

Prediction; Prostate cancer; Machine learning; Prostate biopsy
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