Ultrasound Radiomics for the Prediction of Breast Cancers with HER2-Zero, -Low, and -Positive Status: A Dual-Center Study.

PURPOSE: To assess whether gray-scale ultrasound (US) based radiomic features can help distinguish HER2 expressions (ie, HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. MATERIALS AND METHODS: This retrospective study encompassed female breast cancer patients who underwent US examinations at two distinct centers from February 2021 to July 2023. Tumor segmentation and radiomic feature extraction were performed on grayscale US images. Decision Tree analysis was employed to simultaneously evaluate feature importance, and the Least Absolute Shrinkage and Selection Operator technique was utilized for feature selection to construct the radiomic signature. The Area Under the Curve (AUC) of the Receiver Operating Characteristic curve was employed to assess the performance of the radiomic features. Multivariate logistic regression was used to identify independent predictors for distinguishing HER2 expression in the dataset. RESULTS: The training set comprised 292 patients from Center 1 (median, 51 years; interquartile range [IQR]: 45-61), while the external validation set included 131 patients from Center 2 (median, 51 years; IQR: 45-62). In the external validation dataset, the radiomic features achieved AUC of 0.76 for distinguishing between HER2-low and positive tumors versus HER2-zero tumors. The AUC for differentiating HER2-low (1+) from HER2-zero tumors was 0.74, and for distinguishing HER2-low (2+) from HER2-zero tumors, the AUC was 0.77. In the multivariate analysis assessing HER2-low and HER2-positive versus HER2-zero tumors, internal echoes (P = .029) and margins (P < .001) emerged as independent predictive factors. CONCLUSION: The radiomic signature and tumor descriptors from gray-scale US may predict distinct HER2 expressions of breast cancers with therapeutic implications.

Integrated nomogram to predict HER2 expression in breast tumor: Clinical, Ultrasound, and Photoacoustic imaging approaches.

BACKGROUND: HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights. MATERIALS AND METHODS: A total of 301 breast tumors were included in this study, divided into HER2-positive (3+ or 2+ with gene amplification) and HER2-negative (below 3+ and 2+ without gene amplification) groups. Samples were split into training and validation sets in a 7:3 ratio. Statistical analyses involved t-tests, chi-square tests, and rank-sum tests. Predictive factors were identified using univariate and multivariate logistic regression, leading to the creation of three models: ModA (clinical factors only), ModB (clinical plus ultrasound factors), and ModC (clinical, ultrasound, and photoacoustic imaging-derived oxygen saturation (SO2)). RESULTS: The area under the curve (AUC) for ModA was 0.756 (95 % CI: 0.69-0.82), ModB increased to 0.866 (95 % CI: 0.82-0.91), and ModC showed the highest performance with an AUC of 0.877 (95 % CI: 0.83-0.92). These results indicate that the comprehensive model combining clinical, ultrasound, and photoacoustic imaging data (ModC) performed best in predicting HER2 expression. CONCLUSION: The findings suggest that integrating clinical, ultrasound, and photoacoustic imaging data significantly enhances the accuracy of predicting HER2 expression. For personalised breast cancer treatment, the integrated model could provide a comprehensive and reproducible decision support tool.