A Bayesian Gamma-power-mixture survival regression model: predicting the recurrence of prostate cancer post-prostatectomy

TL;DR

This study introduces a Bayesian Gamma-power-mixture survival regression model to predict prostate cancer recurrence, demonstrating that blood-based biomarkers significantly improve predictive information over other clinical variables.

Contribution

The paper develops a novel Bayesian Gamma-power-mixture model for survival analysis and shows its effectiveness in extracting more information from blood biomarkers for prostate cancer recurrence prediction.

Findings

Blood-based biomarkers increase the apparent Shannon information about recurrence.

Blood biomarkers outperform Gleason grades and MRI findings in predictive power.

Selected biomarkers include TGFbeta1, VCAM1, IL6sR, and uPA.

Abstract

In a dataset of 423 patients who had had radical prostatectomy for localised prostate cancer we estimated the apparent Shannon information (ASI) about time to biochemical recurrence in various subsets of the available pre-op variables using a Bayesian Gamma-power-mixture survival regression model. In all the subsets examined the ASI was positive with posterior probability greater than 0.975 . Using only age and results of pre-operative blood tests (PSA and biomarkers) we achieved 0.232 (0.180 to 0.290) nats ASI (0.335 (0.260 to 0.419) bits) (posterior mean and equitailed 95% posterior confidence intervals). This is more than double the mean posterior ASI previously achieved on the same dataset by a subset of the current authors using a log-skew-Student-mixture model, and is greater than that previous value with posterior probability greater than 0.99 . Additionally using pre- or post-operative Gleason grades, operative findings, clinical stage, and presence or absence of extraprostatic extension or seminal vesicle invasion did not increase the ASI extracted. However removing the blood-based biomarkers and replacing them with either pre-operative Gleason grades or findings available from MRI scanning greatly reduced the available ASI to respectively 0.077 (0.038 to 0.120) and 0.088 (0.045 to 0.132) nats (both less than the values using blood-based biomarkers with posterior probability greater than 0.995). A greedy approach to selection of the best biomarkers gave TGFbeta1, VCAM1, IL6sR, and uPA in descending order of importance from those examined.