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Diagnostic performance of contrast‐enhanced mammography for suspicious findings in dense breasts: A systematic review and meta‐analysis

Shuting Lin, Shuting Lin, Hong‐jiang Li, Yizhong Li, Qianqian Chen, Jiayi Ye, Shu Lin, Shu Lin, Si-Qing Cai, Jian‐guo Sun

2024Cancer Medicine11 citationsDOIOpen Access PDF

Abstract

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Topics & Concepts

Meta-analysisMammographyContrast (vision)MedicineRadiologyMedical physicsComputer scienceBreast cancerArtificial intelligencePathologyCancerInternal medicineDigital Radiography and Breast ImagingBreast Lesions and CarcinomasAI in cancer detection