Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm - Lin, Fan; Wang, Zhongyi; Zhang, Kun; Yang, Ping; Ma, Heng; Shi, Yinghong; Liu, Meijie; Wang, Qinglin; Cui, Jingjing; Mao, Ning; Xie, Haizhu (Frontiers in Oncology, 2020)
Objectives To 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. Methods This 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). Results Nineteen 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. Conclusions The radiomics nomogram incorporated with CESM-based radiomics features, BI-RADS category and age could identify benign and malignant breast lesions of sub-1 cm.