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Related Experiment Video

Updated: May 31, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Development and External Validation of a Machine Learning-Based Risk Classification Score for Prostate

Nickolas Stabellini1,2,3, Tarek Nahle3, Viraj Shah3

  • 1Case Western Reserve University, Cleveland, OH.

JCO Oncology Practice
|May 29, 2026
PubMed

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Summary
This summary is machine-generated.

New prostate cancer (PC) cardiovascular risk scores use readily available data, improving risk assessment for patients. These PC-specific scores aid oncologists in identifying high-risk individuals for better cardiovascular care.

Area of Science:

  • Oncology
  • Cardiovascular Medicine
  • Biostatistics

Background:

  • Patients with prostate cancer (PC) have increased cardiovascular (CV) risk.
  • Existing CV risk scores are often inaccurate for PC patients and require unavailable lab tests.
  • This limits practical CV risk assessment in oncology settings.

Purpose of the Study:

  • To develop and validate novel, PC-specific cardiovascular risk scores.
  • To utilize variables routinely available in oncology clinics for risk stratification.
  • To improve the calibration and applicability of CV risk assessment in PC management.

Main Methods:

  • Developed risk scores using XGBoost and LASSO on a development cohort (n=1,815).
  • Validated scores in two independent cohorts (V1: n=4,022; V2: n=1,729).

Related Experiment Videos

Last Updated: May 31, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

  • Assessed performance using time-dependent AUC (TDAUC) for cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF).

    • Main Results:

    • Composite CVD score achieved 10-year TDAUC of 0.71 in the development cohort and 0.59 in V2.
    • ASCVD and HF scores demonstrated good discrimination, with 10-year TDAUCs of 0.66 and 0.70 respectively in the development cohort.
    • Scores retained predictive ability in validation cohorts, indicating robustness.

    Conclusions:

    • These are the first PC-specific CV risk scores developed using oncology-available variables.
    • The scores support practical CV risk stratification within PC clinics.
    • Further validation and implementation are encouraged to enhance patient care.