Predicting hormone receptor status in tumors: An innovative approach using breast ultrasound-radiomics combined model.

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Abstract

Hormone receptor (HR) status is a critical biomarker used to formulate treatment programs and prognosis in breast cancer. Traditional immunohistochemistry relies on invasive tissue samples and may not accurately reflect tumor heterogeneity. Radiomics is a noninvasive technique that involves extracting quantitative imaging characteristics from molecular profiles. The purpose of this study is to develop a combined ultrasound (US)-radiomics model to predict HR status in invasive breast cancer. A retrospective cohort of 186 patients with invasive breast carcinoma, which had been pathologically confirmed, was used in this study, comprising 150 cases (HR-positive (ER+/PR-, HER-2-)) and 36 cases (HR-negative (ER-/PR-, HER-2-)). B-mode US images of the tumor regions were manually segmented, and 463 radiomic features were obtained. T tests, ANOVA, and recursive methods were applied to create a list of features. A support vector machine with a radial basis function kernel was trained using leave-one-out cross-validation. To measure model performance, accuracy, sensitivity, specificity, area under the curve (AUC), and 95% confidence intervals (CIs) were used. The hybrid model had an AUC of 0.728 (95% CI: 0.701-0.755) and an accuracy of 67.9. The model with the highest AUC (0.753, 95% CI: 0.7240.782) was the internal echo-based model. HR-negative tumors were larger, had higher marker of proliferation (Ki-67) indices, and showed greater textural heterogeneity than HR-positive lesions (P < .05). US-radiomics combined modeling is a promising, cost-effective, and radiation-free approach to noninvasive imaging that can predict HR status in breast cancer. US biomarkers can be quantitative, providing insights into tumor microstructure to personalize diagnostic and therapeutic approaches.

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