Comparison of Clinical Performance Between Digital Breast Tomosynthesis and MammouS-N

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Authors: Sung Ui Shin, Mijung Jang, Bo La Yun, Su Min Cho, Yoon Yeong Choi, Bohyoung Kim, Min Jung Kim, Sun Mi Kim
Cited by: 0
References: 21

Abstract

Background/Objectives: We compared the visibility of breast cancer using the newly developed standing automated breast ultrasound system (MammouS-N) and digital breast tomosynthesis (DBT), and identified factors influencing lesion visibility. Methods: We prospectively enrolled 100 women (mean age: 51.6 years; range: 26–76 years) who were diagnosed with breast cancer and were scheduled to undergo DBT between January and July 2024. They underwent DBT and an ultrasound on the same day. Two radiologists evaluated the visibility scores (0–5) of lesions corresponding to biopsy-confirmed breast cancers identified using magnetic resonance imaging. The Wilcoxon signed-rank test was used to compare the visibility scores of cancers identified on DBT and/or MammouS-N images. Results: Among the 100 women, invasive ductal carcinoma was the most common malignancy (73%). DBT findings included negative findings (7%), masses (46%), masses with calcification (29%), calcifications only (15%), and architectural distortions (3%). On MammouS-N ultrasound, most lesions were classified as masses (93%), whereas 7% were non-mass lesions. For Reviewer 1, MammouS-N demonstrated significantly higher visibility scores (higher scores: 26 on MammouS-N, seven on DBT; equal scores: 67, z = −3.234, p = 0.001). For Reviewer 2, the two modalities showed no significant difference in visibility (higher scores: 27 on MammouS-N, 28 on DBT, equal scores: 45, z = −0.040, p = 0.968). Noncalcified lesions that were obscured on DBT were better visualized on MammouS-N (p < 0.001) by both reviewers. Conclusions: MammouS-N holds promise as an imaging modality complementary to DBT in women with dense breast tissue, particularly for non-calcified lesion detection.

Fields of Study & Subjects

Digital Radiography and Breast Imaging AI in cancer detection Breast Lesions and Carcinomas

Publication details

Journal / source
Tomography
Volume
12
Issue
2
Pages
17–17
Publication date
30 January 2026
Language
EN