Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records.

ABSTRACT:

Background

Prostate cancer (PCa) screening is not routinely conducted in men aged 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) and patient data applied toward early prediction of PCa may lead to earlier interventions and increased survival. We have developed machine learning (ML) models to predict PCa risk in men 55 and under using PRSs combined with patient data.

Methods

We conducted a retrospective study on 91,106 male patients aged 35-55 using the UK Biobank database. Five gradient boosting models were developed and validated utilizing routine screening data, PRSs, additional clinical data, or combinations of the three.

Results

Combinations of PRSs and patient data outperformed models that utilized PRS or

SUBMITTER: Varma A 

PROVIDER: S-EPMC9844630 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature