Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Lanyun Wang; Wenjun Yang; Xiaoli Xie; Weiyan Liu; Hao Wang; Jinjiang Shen; Yi Ding; Bei Zhang; Bin Song

doi:10.21037/gs-20-473

Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Gland Surg. 2020 Dec;9(6):2005-2016. doi: 10.21037/gs-20-473.

Authors

Lanyun Wang¹, Wenjun Yang¹, Xiaoli Xie², Weiyan Liu³, Hao Wang¹, Jinjiang Shen¹, Yi Ding¹, Bei Zhang⁴, Bin Song¹

Affiliations

¹ Department of Radiology, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China.
² Department of Pathology, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China.
³ Department of General Surgery, Minhang Branch, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: This study aimed to investigate the diagnostic performance of radiomic features based on digital mammography (DM) in the differential diagnosis of benign and malignant round-like (round and oval) solid tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications and to investigate whether quantitative radiomic features can distinguish triple-negative breast cancer (TNBC) from non-TNBC (NTNBC).

Methods: This retrospective study included 112 patients with round-like tumors who underwent DM within 20 days preoperatively. Breast masses were segmented manually on the DM images, then radiomic features were extracted. The predictive models were used to distinguish between benign and malignant tumors and to predict TNBC in invasive ductal carcinoma. The receiver operating characteristic curves (ROCs) for these models were obtained for initial DM characteristics, radiomic features to predict malignant tumors and TNBC. The decision curve was obtained to evaluate the clinical usefulness of the model for the prediction of benign or malignant tumors.

Results: The study cohort included 79 patients with pathologically confirmed malignant masses and 33 patients with benign (training cohort: n=79; testing cohort: n=33). A total of 396 features were extracted from the DM images for each patient. The radiomics model for the prediction of malignant tumors achieved an area under the receiver operating characteristic curve (AUC) of 0.88 [95% confidence interval (CI), 0.76-1.00] in the testing cohort; the radiomics model for the prediction of TNBC achieved an AUC of 0.84 (95% CI, 0.73-0.96). In contrast, DM characteristics alone poorly predicted malignant tumors, with the density achieving an AUC 0.69 (95% CI, 0.59-0.79); there was no significant difference in DM characteristics between TNBC and NTNBC (P>0.05, all). The decision curve showed the good clinical usefulness of the model for the prediction of malignant tumors.

Conclusions: This study showed that DM-based radiomics can accurately discriminate between benign and malignant round-like tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications. Additionally, it can be used to predict TNBC in invasive ductal carcinoma. DM-based radiomics can aid radiologists in mammogram reading, clinical diagnosis and decision-making.

Keywords: Digital mammography (DM); molecular subtypes; radiomics; round-like breast tumors.