Abstract
OBJECTIVES: Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively. METHODS: Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction. RESULTS: The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test cohorts, the patient-level combined model showed the best discrimination ability with the macro-AUCs of 0.988 (95% CI: 0.983-0.992), 0.915 (95% CI: 0.851-0.965), and 0.862 (95% CI: 0.820-0.899), respectively. CONCLUSION: This study built a robust and interpretable clinical-radiomics model to evaluate three classes of HER2 status based on ultrasound images. CRITICAL RELEVANCE STATEMENT: Ultrasound-based radiomics method can noninvasively identify three different states of HER2, which may guide treatment decisions and the implementation of personalized HER2-targeted treatment for invasive breast cancer patients. KEY POINTS: Determination of HER2 status can affect treatment options for breast cancer. The ultrasound-based clinical-radiomics model can discriminate the three different HER2 statuses. Our developed model can assist in providing personalized recommendations for novel HER2-targeted therapies.