Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss.

ABSTRACT:

Purpose

To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer.

Methods

The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performa

SUBMITTER: Chu M 

PROVIDER: S-EPMC10174287 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature