Prediction System for Prostate Cancer Recurrence Using Machine Learning

• Main Authors: Sun Jung Lee, Sung Hye Yu, Yejin Kim, Jae Kwon Kim, Jun Hyuk Hong, Choung-Soo Kim, Seong Il Seo, Seok-Soo Byun, Chang Wook Jeong, Ji Youl Lee, In Young Choi
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Applied Sciences
• Subjects: prostate cancer; machine learning; prediction; clinical decision support system; gradient boost
• Online Access: https://www.mdpi.com/2076-3417/10/4/1333

Prostate cancer is the fourth most common cancer affecting South Korean males, and the biochemical recurrence (BCR) of prostate cancer occurs in approximately 25% of patients five years after radical prostatectomy. The ability to predict BCR would help clinicians and patients to make better treatment decisions. Therefore, in this study, we have proposed a web-based clinical decision support system that predicts the BCR of prostate cancer in Korean patients. The data were obtained from the Korean Prostate Cancer Registry (KPCR) database, which contained information about 7394 patients with prostate cancer who were treated at one of the six major medical institutions in South Korea between May 2001 and December 2014. We tested 13 prediction models and selected the gradient boosting classifier because it demonstrated excellent prediction performance. Using this model, we were able to create a web application and once clinical data from patients were entered, the three- and five-year post-surgery BCR predictions could be extracted. We developed a clinical decision support system to provide a prostate cancer BCR predictive function to facilitate postoperative follow-up and clinical management. This system will help clinicians develop a strategic approach for prostate cancer treatment by predicting the likelihood of prostate cancer recurrence.