Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features.

Objective: To evaluate the human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer using multidetector computed tomography (MDCT)-based handcrafted and deep radiomics features. Methods: This retrospective study enrolled 339 female patients (primary cohort, n=177; validation cohort, n=162) with pathologically confirmed invasive breast cancer. Handcrafted and deep radiomics features were extracted from the MDCT images during the arterial phase. After the feature selection procedures, handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression analysis. Performance was assessed by measures of discrimination, calibration, and clinical usefulness in the primary cohort and validated in the validation cohort. Results: The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739 [95% confidence interval (95% CI): 0.661-0.818] in the primary cohort and 0.695 (95% CI: 0.609-0.781) in the validation cohort. The deep radiomics signature also had a discriminative ability with a C-index of 0.760 (95% CI: 0.690-0.831) in the primary cohort and 0.777 (95% CI: 0.696-0.857) in the validation cohort. The combined model, which incorporated both the handcrafted and deep radiomics signatures, showed good discriminative ability with a C-index of 0.829 (95% CI: 0.767-0.890) in the primary cohort and 0.809 (95% CI: 0.740-0.879) in the validation cohort. Conclusions: Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast cancer. Thus, these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.