Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling

• Published in: Journal of magnetic resonance imaging 2024-05
• Main Authors: Zhou, Jiejie, Zhang, Yang, Miao, Haiwei, Yoon, Ga Young, Wang, Jinhao, Lin, Yezhi, Wang, Hailing, Liu, Yan-Lin, Chen, Jeon-Hor, Pan, Zhifang, Su, Min-Ying, Wang, Meihao
• Format: Article
• Language: English
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29447

Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). Retrospective. 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values