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

Autumn O’Donnell, Eric Wolsztynski, Michael Cronin, Shirin Moghaddam

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1276
JournalCancers
Volume15
Issue number4
DOIs
Publication statusPublished - Feb 2023

Keywords

  • biochemical recurrence
  • bioinformatics
  • genomics
  • machine learning
  • personalised medicine
  • prediction
  • prostate cancer
  • statistical modelling
  • survival analysis

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