AUTHOR=Xue Jing , Li Yilun , Qu Tianyun , Qin Yidi , Wang Haoqi , Rong Xiaocui , Tian Jingliu , Wang Tao , Zhang Jianhua , Li Zhigang , Ping Yong 

TITLE=The clinical validity of radiomics-based prediction of molecular subtypes in breast cancer from digital mammary tomosynthesis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1661116

DOI=10.3389/fonc.2025.1661116

ISSN=2234-943X

ABSTRACT=ObjectiveTo explore the use of digital breast tomography (DBT) imaging omics in developing breast cancer (BC) diagnostic models to identify molecular subtype characteristics of BC.MethodsA retrospective analysis was conducted on 433 DBT images. Candidate features were extracted, and least absolute shrinkage and selection operator (LASSO) regression model was established. Within the training set, machine learning (ML) models were constructed, and their predictive performance was evaluated using receiver operating characteristic (ROC) curves and confusion matrixes in the test set, thereby screening the best predictive classifier. Univariate and multivariate Cox regression analyses were conducted to obtain key characteristics of nomogram modeling, correction and decision curve analysis (DCA) were used to evaluate the clinical potential of this model.ResultsThe LASSO selected 14 features. Random Forest (RF) had the highest AUC value, the highest accuracy, sensitivity, recall rate and F1 score on the training set and test set, and was the best classifier. A nomogram model was established. The odds ratio (OR) of BC patients increased with the increase of the total score.ConclusionThe key features of BC were revealed by image omics and ML models, and a nomogram model with diagnostic value was constructed.