Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm.

Fan Lin,Yinghong Shi,Jingjing Cui,Ning Mao,Haizhu Xie,Kun Zhang,Ping Yang,Qinglin Wang,Heng Ma,Meijie Liu,Zhongyi Wang

doi:10.3389/fonc.2020.573630

Abstract

ObjectivesTo develop a radiomics nomogram that incorporates contrast-enhanced spectral mammography (CESM)-based radiomics features and clinico-radiological variables for identifying benign and malignant breast lesions of sub-1 cm.MethodsThis retrospective study included 139 patients with the diameter of sub-1 cm on cranial caudal (CC) position of recombined images. Radiomics features were extracted from low-energy and recombined images on CC position. The variance threshold, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select optimal predictive features. Radiomics signature (Rad-score) was calculated by a linear combination of selected features. The independent predictive factors were identified by ANOVA and multivariate logistic regression. A radiomics nomogram was developed to predict the malignant probability of lesions. The performance and clinical utility of the nomogram was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsNineteen radiomics features were selected to calculate Rad-score. Breast imaging reporting and data system (BI-RADS) category and age were identified as predictive factors. The radiomics nomogram combined with Rad-score, BI-RADS category, and age showed better performance (area under curves [AUC]: 0.940, 95% confidence interval [CI]: 0.804–0.992) than Rad-score (AUC: 0.868, 95% CI: 0.711–0.958) and clinico-radiological model (AUC: 0.864, 95% CI: 0.706–0.956) in the validation cohort. The calibration curve and DCA showed that the radiomics nomogram had good consistency and clinical utility.ConclusionsThe radiomics nomogram incorporated with CESM-based radiomics features, BI-RADS category and age could identify benign and malignant breast lesions of sub-1 cm.

Highlights

Breast cancer is a malignant tumor that endangers women’s health and quality of life
Breast imaging reporting and data system (BI-RADS) category and age were identified as predictive factors
The radiomics nomogram combined with Rad-score, breast imaging reporting and data system (BI-RADS) category, and age showed better performance than Rad-score (AUC: 0.868, 95% CI: 0.711– 0.958) and clinico-radiological model (AUC: 0.864, 95% CI: 0.706–0.956) in the validation cohort

Summary

Introduction

Breast cancer is a malignant tumor that endangers women’s health and quality of life. The development of medical imaging technology and the widespread use of breast cancer screening have gradually increased the detection rate of small breast lesions [1]. Malignant signs are not obvious due to the lack of specificity in imaging features. Existing imaging methods have difficulty making accurate qualitative diagnosis; breast lesions recognized as breast imaging reporting and data system (BI-RADS) category 4 or 5 are usually recommended for biopsy [2]. The small size of lesions brings difficulty for clinicians in performing a successful biopsy, and as an invasive examination, biopsy has the risk of causing serious complications, such as severe bleeding and infection [4, 5]. Using non-invasive methods to discriminate the nature of small lesions and help radiologists and clinicians make accurate diagnosis and clinical decision is important

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