Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning

Simple Summary Prostate cancer is among the most prevalent cancers for men globally, accounting for 13% of cancer diagnoses in the male population each year. Surgical intervention is the primary treatment option but fails in up to 40% of patients, who experience biochemical recurrence (BCR). Determining the likelihood of recurrence and the length of time between surgery and BCR is critical for patient treatment decision-making. Traditional predictive models exploit routine clinical variables such as cancer stage, and may be improved upon by leveraging other accessible information about the patient. This study considers including patient-specific genomic data to identify relevant additional predictors of BCR-free survival, which requires the use of modern machine learning techniques. The results of this study indicate that including such genomic data leads to a gain in BCR prediction performance over models using clinical variables only. Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.