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

To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer. The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factor...

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Published inFrontiers in oncology Vol. 13; p. 1182792
Main Authors Chu, Meiling, Zhou, Yue, Yin, Yulian, Jin, Lan, Chen, Hongfeng, Meng, Tian, He, Binjun, Wu, Jingjing, Ye, Meina
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 27.04.2023
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Summary:To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer. 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 performance of the model in the test dataset. 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 ( < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 0.716, 0.691). 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.
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These authors have contributed equally to this work
Edited by: Zheng Wang, Shanghai Jiao Tong University, China
Reviewed by: Hangcheng Fu, University of Louisville, United States; Xinyuan Ding, Suzhou Municipal Hospital, China
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2023.1182792