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Related Experiment Video

Updated: Jun 7, 2025

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

Published on: March 6, 2018

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine

Deepika Gopukumar^1,2, Nirup Menon³, Martin W Schoen^2,4

¹Richard A Chaifetz School of Business, Saint Louis University, St. Louis, MO, United States.

JMIR Medical Informatics

|November 19, 2024

Summary

Enzalutamide offers more survival days for metastatic castration-resistant prostate cancer (mCRPC) patients than abiraterone. Machine learning identified patient subgroups and created prescription rules to personalize treatment and improve survival duration.

Keywords:

causal survival forest heterogeneity machine learning metastatic castration resistant prostate cancer oncology pharmacology prescription policy tree prostate cancer

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Published on: January 8, 2020

Area of Science:

Oncology
Medical Informatics
Biostatistics

Background:

Metastatic castration-resistant prostate cancer (mCRPC) has a high mortality rate, with androgen receptor pathway inhibitors (ARPIs) like abiraterone and enzalutamide being preferred over docetaxel due to lower toxicity.
Identifying patient-level heterogeneity in treatment response is crucial for optimizing survival duration in mCRPC.
No prior studies have utilized machine learning to establish heterogeneous treatment rules for mCRPC based on patient data.

Purpose of the Study:

To quantify patient-level heterogeneity in the association between prescribed medication and overall survival duration (follow-up days) in mCRPC patients.
To develop personalized medication prescription rules using patient demographics, test results, and comorbidities.

Main Methods:

Analysis of 2886 US veterans with mCRPC treated with either abiraterone or enzalutamide as first-line therapy from 2014-2020.
Exclusion of patients treated with docetaxel, cabaxitaxel, mitoxantrone, or sipuleucel-T.
Application of causal survival forests to estimate treatment effects and construct a prescription policy tree.

Main Results:

Enzalutamide was associated with an average of 59.94 more survival days compared to abiraterone.
Significant patient-level heterogeneity was observed in treatment effects across demographics, test results, and comorbidities.
Two distinct patient subgroups were identified using augmented inverse-propensity weighting (AIPW) scores, indicating differential treatment benefits.

Conclusions:

Evidence of heterogeneity in mCRPC treatment suggests personalized prescription rules can improve survival.
Machine learning-derived rules based on patient characteristics, lab results, and comorbidities can guide optimal medication selection.
Personalized treatment decisions hold the potential to enhance survival duration for mCRPC patients.