Multiparametric MRI-based radiomics nomogram for noninvasive stratification of HER2 expression status in breast
Ting Zhan1,2, Xiaofei Tang3, Jiankun Dai4
1Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Background:
Accurate assessment of human epidermal growth factor receptor 2 (HER2) status, particularly HER2-low (formerly HER2-negative), is critical for guiding optimal HER2-targeted therapeutic decisions, as these patients may now be eligible for novel anti-HER2 antibody-drug conjugates. This study aimed to develop a radiomic nomogram based on multiparametric magnetic resonance imaging (MRI)-derived radiomic features combined with clinical characteristics for distinguishing HER2-positive and HER2-low breast cancer (BC) from HER2-negative BC (Task 1) and HER2-low from HER2-negative BC (Task 2).
Methods:
A total of 364 patients from two centers with invasive ductal carcinoma of BC were retrospectively enrolled from September 2022 to March 2024 and divided into two tasks. Patients from Center 1 (The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University) were randomly assigned to training cohort (Task 1: n=165; Task 2: n=112) and internal validation cohort (Task 1: n=71; Task 2: n=48). Patients from Center 2 (Ganzhou Cancer Hospital) were used as an external validation cohort (Task 1: n=78; Task 2: n=52). Radiomics signatures (RS) models were established using features from dynamic contrast-enhanced (DCE), T2-weighted image (T2WI), and combination (RS-Com). A clinical characteristic model was established through univariate and multivariate analyses, and a radiomics nomogram was developed by integrating radiomics score (Rad-score) with clinically significant characteristics. Its performance was evaluated through metrics such as the area under the curve (AUC), calibration assessment, and decision curve analysis (DCA).
Results:
For Task 1, RS-Com yielded a greater AUC for training, internal, and external validation of (0.861, 0.784, and 0.794, respectively) than did RS-DCE (AUC =0.743, 0.732, and 0.629, respectively) and RS-T2WI (AUC =0.741, 0.663, and 0.652 respectively). For Task 2, compared with RS-DCE (AUC =0.774/0.668/0.738) and RS-T2WI (AUC =0.771/0.677/0.637), RS-Com also exhibited greater AUCs for training, internal, and external validation (0.822/0.725/0.773). Univariate and multivariate analyses showed that the estrogen receptor (ER) and progesterone receptor (PR) statuses were independent predictors for distinguishing HER2 status. For both Tasks 1 and 2, the radiomic nomogram demonstrated the best performance with AUCs of 0.916/0.940/0.820 and 0.863/0.892/0.833, respectively.
Conclusions:
The multiparametric MRI-based radiomic nomogram can more accurately categorize the levels of HER2 expression in invasive ductal carcinoma patients, especially for those with HER2-low expression, serving as an early-stage aid for clinicians to devise tailored and precise therapeutic strategies.
