Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study.

ABSTRACT:

Background

The predictive power of the treatment efficacy and prognosis in primary androgen deprivation therapy (ADT) for advanced prostate cancer is not satisfactory. The objective of this study was to integrate genetic and clinical data to predict castration resistance in primary ADT for advanced prostate cancer by machine learning (ML).

Methods

Clinical and single nucleotide polymorphisms (SNP) data obtained in the KYUCOG-1401-A study (UMIN000022852) that enrolled Japanese patients with advanced prostate cancer were used. All patients were treated with primary ADT. A point-wise linear (PWL) algorithm, logistic regression with elastic-net regularization, and eXtreme Gradient Boosting were the ML algorithms used in this study. Area under the curve for castration resistance

SUBMITTER: Shiota M 

PROVIDER: S-EPMC11523954 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature