An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study

Yan Huang; Qin Xiao; Yiqun Sun; Zhe Wang; Qin Li; He Wang; Yajia Gu

doi:10.3389/fonc.2020.607235

An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study

Front Oncol. 2021 Feb 16:10:607235. doi: 10.3389/fonc.2020.607235. eCollection 2020.

Authors

Yan Huang^{1

2}, Qin Xiao^{1

2}, Yiqun Sun^{1

2}, Zhe Wang³, Qin Li^{1

2}, He Wang^{4

5

6}, Yajia Gu^{1

2}

Affiliations

¹ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.
⁴ Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
⁵ Human Phenome Institute, Fudan University, Shanghai, China.
⁶ Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.

Abstract

Purpose: To develop and validate an imaging-radiomics model for the diagnosis of male benign and malignant breast lesions.

Methods: Ninety male patients who underwent preoperative mammography from January 2011 to December 2018 were enrolled in this study (63 in the training cohort and 27 in the validation cohort). The region of interest was segmented into a mediolateral oblique view, and 104 radiomics features were extracted. The minimum redundancy and maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) methods were used to exclude radiomics features to establish the radiomics score (rad-score). Mammographic features were evaluated by two radiologists. Univariate logistic regression was used to select for imaging features, and multivariate logistic regression was used to construct an imaging model. An imaging-radiomics model was eventually established, and a nomogram was developed based on the imaging-radiomics model. Area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the clinical value.

Results: The AUC based on the imaging model in the validation cohort was 0.760, the sensitivity was 0.750, and the specificity was 0.727. The AUC, sensitivity and specificity based on the radiomics in the validation cohort were 0.820, 0.750, and 0.867, respectively. The imaging-radiomics model was better than the imaging and radiomics models; the AUC, sensitivity, and specificity of the imaging-radiomics model in the validation cohort were 0.870, 0.824, and 0.900, respectively.

Conclusion: The imaging-radiomics model created by the imaging characteristics and radiomics features exhibited a favorable discriminatory ability for male breast cancer.

Keywords: diagnosis; male breast lesions; malignant; mammography; radiomics.