AUTHOR=Chu Meiling , Zhou Yue , Yin Yulian , Jin Lan , Chen Hongfeng , Meng Tian , He Binjun , Wu Jingjing , Ye Meina 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1182792

DOI=10.3389/fonc.2023.1182792

ISSN=2234-943X

ABSTRACT=Purpose: To establish a high-risk prediction model for Aromatase Inhibitors-associated Bone Loss (AIBL) in patients with hormone receptor-positive breast cancer.
Methods: The study included breast cancer patients who received treatment with Aromatase Inhibitors (AIs). 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 LASSO regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset.
Results: A total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, Hip Fracture index, Major Osteoporotic Fracture index, prolactin (PRL) and osteocalcin (OC) were found to be independent risk factors for AIBL (P<0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs 0.716, 0.691). 
Conclusion: The XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.