Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

Abstract

Objective

This study aimed to develop and validate a machine learning model for predicting bone metastases (BM) in prostate cancer (PCa) patients.

Methods

Demographic and clinicopathologic variables of PCa patients in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2017 were retrospectively analyzed. We used six different machine learning algorithms, including Decision tree (DT), Random forest (RF), Multilayer Perceptron (MLP), Logistic regression (LR), Naive Bayes classifiers (NBC), and eXtreme gradient boosting (XGB), to build prediction models. External validation using data from 644 PCa patients of the First Affiliated Hospital of Nanchang University from 2010 to 2016. The performance of the models was evaluated using the area under receiver operating characteristic curve (AUC), accuracy score, sensitivity (recall rate) and specificity. A web predictor was developed based on the best performance model.

Results

A total of 207,137 PCa patients from SEER were included in this study. Of whom, 6725 (3.25%) developed BM. Gleason score, Prostate-specific antigen (PSA) value, T, N stage and age were found to be the risk factors of BM. The XGB model offered the best predictive performance among these 6 models (AUC: 0.962, accuracy: 0.884, sensitivity (recall rate): 0.906, and specificity: 0.879). An XGB model-based web predictor was developed to predict BM in PCa patients.

Conclusion

This study developed a machine learning model and a web predictor for predicting the risk of BM in PCa patients, which may help physicians make personalized clinical decisions and treatment strategy for patients.

Keywords:

• prostate cancer
• bone metastasis
• machine learning
• prediction model
• SEER

Acknowledgments

This work is supported by the Department of Science and Technology Program of Jiangxi Province, China (No. 20202BBGL73015, 20203BBG73045) and the project of Jiangxi Provincial Health Commission (No. 20161024).

Data Sharing Statement

The datasets generated and/or analyzed during the current study are available in the SEER database (https://seer.cancer.gov/).

Ethics Approval and Consent to Participate

We received permission to access the research data file in the SEER program from the National Cancer Institute, US. Approval was waived by the local ethics committee, as SEER data is publicly available and de-identified. This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University, and cases from the First Affiliated Hospital of Nanchang University signed written informed consent form. This study followed the guidelines outlined in the Declaration of Helsinki.

Disclosure

The authors report no conflicts of interest in this work.