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CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2024; 34(04): 677-687
DOI: 10.1055/s-0044-1779589
Review Article

Approach to Nonmass Lesions on Breast Ultrasound

Supraja Laguduva Mohan
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Ekta Dhamija 
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Richa Gauba
2   Department of Radiodiagnosis, National Cancer Institute - All India Institute of Medical Sciences, Jhajjar, Haryana, India
› Institutsangaben Funding None.
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Abstract

Nonmass lesions in breast ultrasound (US) are areas of altered echogenicity without definite margins or mass effect. However, these lesions may show calcifications, associated architectural distortion, or shadowing just like masses. They vary in their echogenicity, distribution, ductal or nonductal appearance and the associated features that can be seen in variety of benign and malignant pathologies. With no uniform definition or classification system, there is no standardized approach in further risk categorization and management strategies of these lesions. Malignant nonmass lesions are not uncommon and few sonographic features can help in differentiating benign and malignant pathologies. US-guided tissue sampling or lesion localization can be preferred in the nonmass lesions identified on second look US after magnetic resonance imaging or mammography. This article aims to describe various imaging patterns and attempts to provide an algorithmic approach to nonmass findings on breast US.

Keywords

nonmass lesions - BIRADS - architectural distortion - breast ultrasound

Authors' Contributions

S.L.M. contributed to resources and writing—original draft. E.D. helped in conceptualization, resources, writing—review and editing, and supervision. R.G. was involved in writing—review and editing.




Publikationsverlauf

Artikel online veröffentlicht:
23. Februar 2024

© 2024. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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