Diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in a clinical screening setting—a retrospective study

• Published in: European radiology 2024-08, Vol.34 (8), p.5451-5460
• Main Authors: Klein Wolterink, Femke, Ab Mumin, Nazimah, Appelman, Linda, Derks-Rekers, Monique, Imhof-Tas, Mechli, Lardenoije, Susanne, van der Leest, Marloes, Mann, Ritse M.
• Format: Article
• Language: English
• Subjects: Adult; Aged; Automation; Biopsy; Breast; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - diagnostic imaging; Breasts; Cancer; Cancer screening; Data systems; Density; Diagnostic Radiology; Digital imaging; Early Detection of Cancer - methods; Female; Humans; Imaging; Imaging, Three-Dimensional - methods; Internal Medicine; Interventional Radiology; Lesions; Mammography; Mammography - methods; Medical imaging; Medical screening; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Radiology; Recall; Reproducibility of Results; Retrospective Studies; Ultrasonic imaging; Ultrasonography, Mammary - methods; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10568-5

Objectives To assess the diagnostic performance of 3D automated breast ultrasound (3D-ABUS) in breast cancer screening in a clinical setting. Materials and methods All patients who had 3D-ABUS between January 2014 and January 2022 for screening were included in this retrospective study. The images were reported by 1 of 6 breast radiologists based on the Breast Imaging Reporting and Data Systems (BI-RADS). The 3D-ABUS was reviewed together with the digital breast tomosynthesis (DBT). Recall rate, biopsy rate, positive predictive value (PPV) and cancer detection yield were calculated. Results In total, 3616 studies were performed in 1555 women (breast density C/D 95.5% ( n  = 3455/3616), breast density A/B 4.0% ( n  = 144/3616), density unknown (0.5% ( n  = 17/3616)). A total of 259 lesions were detected on 3D-ABUS (87.6% ( n  = 227/259) masses and 12.4% ( n  = 32/259) architectural distortions). The recall rate was 5.2% ( n  = 188/3616) (CI 4.5–6.0%) with only 36.7% ( n  = 69/188) cases recalled to another date. Moreover, recall declined over time. There were 3.4% ( n  = 123/3616) biopsies performed, with 52.8% ( n  = 65/123) biopsies due to an abnormality detected in 3D-ABUS alone. Ten of 65 lesions were malignant, resulting in a positive predictive value (PPV) of 15.4% ( n  = 10/65) (CI 7.6–26.5%)). The cancer detection yield of 3D-ABUS is 2.77 per 1000 screening tests (CI 1.30–5.1). Conclusion The cancer detection yield of 3D-ABUS in a real clinical screening setting is comparable to the results reported in previous prospective studies, with lower recall and biopsy rates. 3D-ABUS also may be an alternative for screening when mammography is not possible or declined. Clinical relevance statement 3D automated breast ultrasound screening performance in a clinical setting is comparable to previous prospective studies, with better recall and biopsy rates. Key Points • 3D automated breast ultrasound is a reliable and reproducible tool that provides a three-dimensional representation of the breast and allows image visualisation in axial, coronal and sagittal. • The diagnostic performance of 3D automated breast ultrasound in a real clinical setting is comparable to its performance in previously published prospective studies, with improved recall and biopsy rates. • 3D automated breast ultrasound is a useful adjunct to mammography in dense breasts and may be an alternative for screening when mammography is not possible or declined.