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Updated: Sep 3, 2026
Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
Published on: December 5, 2017
Noninvasive stratification of HER2 status in breast cancer using multiparametric DECT: a prospective cohort study
Huifang Chen1, Yao Huang1,2, Xiangfei Zeng1
1Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
Background:
HER2 heterogeneity in breast cancer requires precise stratification for treatment guidance. Differences in dual-energy computed tomography (DECT)-derived parameters among HER2-zero, HER2-low, and HER2-positive breast cancers remain unknown. We aimed to evaluate the performance of DECT-based quantitative parameters to distinguish HER2-zero, HER2-low, and HER2-positive expression status in breast cancer, and to compare the efficacy between DECT-predicted HER2 status and pathological HER2 status in predicting pathologic complete response (pCR).
Methods:
A total of 469 breast cancer patients who underwent chest DECT for staging (November 2020-May 2023) were prospectively recruited. DECT quantitative parameters, such as the normalized iodine concentration (NIC), the normalized effective atomic number (nZeff), the slope of the spectral Hounsfield unit curve (λHu), and polyenergetic images (PEI), were obtained from reconstructed images. Decision tree models were constructed to classify the HER2-zero, HER2-low, and HER2-positive status based on six DECT parameters. These models were tested for performance using the area under the receiver operating characteristic curve (AUC) in training (n = 281) and validation (n = 188) cohorts. Efficacy analysis was performed to compare the efficacy of the DECT-predicted HER2 status with pathological HER2 status in predicting pCR.
Results:
Statistical analysis revealed significant differences in arterial and venous phase NIC, ΔNIC, venous phase λHu, ΔλHu, and venous phase PEI (HU) among HER2 subtypes (all P < 0.001). The decision tree model achieved AUCs of 0.821 (HER2-zero), 0.848 (HER2-low), and 0.899 (HER2-positive) in training, with validation cohort AUCs of 0.825, 0.850, and 0.879, respectively. DECT-predicted HER2 combined with estrogen/progesterone receptor status demonstrated superior pCR prediction (AUC: 0.760) compared to pathological HER2-based models (AUC: 0.689; P = 0.010).
Conclusions:
DECT-based quantitative parameters enable noninvasive stratification of HER2-zero, HER2-low, and HER2-positive breast cancers. Additionally, HER2 predicted by DECT outperformed conventional pathological HER2 status in predicting pCR in patients undergoing neoadjuvant chemotherapy.