Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Quantitative Imaging in Medicine and Surgery

article

Simin Wang¹  Yajia Gu¹  Yuqi Sun³  Ning Mao⁴  Shaofeng Duan⁵  Qin Li¹  Ruimin Li¹  Tingting Jiang¹  Zhongyi Wang⁴  Haizhu Xie⁴ 

[1] Department of Radiology, Fudan University Shanghai Cancer Center;Department of Oncology, Shanghai Medical College, Fudan University;Department of Biostatistics, School of Public Health, Fudan University;Department ofRadiology, Yantai Yuhuangding Hospital, Qingdao University;GE Healthcare China

关键词: Contrast-enhanced mammography (CEM);    radiomics;    breast;    Breast Imaging Reporting and Data System (BI-RADS);    classification;   

DOI  :  10.21037/qims-21-103

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions. Methods: This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlation and Boruta’s approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests. Results: A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918–1.000) for the combined model, 0.947 (95% CI: 0.891–0.997) for the radiomics model, and 0.882 (95% CI: 0.803–0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050). Conclusions: Incorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions.

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