Enhancing Early Breast Cancer Diagnosis With Contrast-Enhanced Ultrasound Radiomics: Insights From Intratumoral and Peritumoral Analysis.

Abstract

Introduction: To develop and validate contrast-enhanced ultrasound (CEUS) radiomics model for the accurate diagnosis of breast cancer by integrating intratumoral and peritumoral regions.

Materials and methods: This study enrolled 333 patients with breast lesions from Shenzhen people's hospital between March 2022 and March 2024. Radiomics features were extracted from both intratumoral and peritumoral (3 mm) regions on CEUS images. Significant features were identified using the Mann-Whitney U test, Spearman's correlation coefficient, and least absolute shrinkage and selection operator logistic regression. These features were used to construct radiomics models. The model's performance was evaluated using the area under the receiver operating characteristic curve, area under curve (AUC), decision curve analysis, and calibration curves.

Results: The radiomics models demonstrated robust diagnostic performance in both the training and testing sets. The model that combined intratumoral and peritumoral features showed superior predictive accuracy, with AUCs of 0.872 (95% CI: 0.829, 0.915) and 0.863 (95% CI: 0.770, 0.956), respectively, compared to the intratumoral model alone. Calibration curves indicated excellent agreement between predicted and observed outcomes, with Hosmer-Lemeshow test P = .97 and P= .62 for the both the training and testing sets, respectively. decision curve analysis revealed that the combined model provided significant clinical benefits across a wide range of threshold probabilities, outperforming the intratumoral model in both sets.

Conclusion: The radiomics model integrating intratumoral and peritumoral features shows significant potential for the accurate diagnosis of breast cancer, enhancing clinical decision-making and guiding treatment strategies.