Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations

Hee Jeong Kim; Hak Hee Kim; Ki Hwan Kim; Ji Sung Lee; Woo Jung Choi; Eun Young Chae; Hee Jung Shin; Joo Hee Cha; Woo Hyun Shim

doi:10.1007/s00330-024-10718-3

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations

Eur Radiol. 2024 Apr 3. doi: 10.1007/s00330-024-10718-3. Online ahead of print.

Authors

Hee Jeong Kim¹, Hak Hee Kim², Ki Hwan Kim³, Ji Sung Lee⁴, Woo Jung Choi¹, Eun Young Chae¹, Hee Jung Shin¹, Joo Hee Cha¹, Woo Hyun Shim¹

Affiliations

¹ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
² Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea. hhkim@amc.seoul.kr.
³ Lunit Inc., 15F, 27, Teheran-Ro 2-Gil, Gangnam-Gu, Seoul, 06241, South Korea.
⁴ Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College, Ulsan, South Korea.

PMID: 38570382
DOI: 10.1007/s00330-024-10718-3

Abstract

Objectives: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions.

Methods: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis.

Results: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both).

Conclusion: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions.

Clinical relevance statement: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes.

Key points: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Keywords: Artificial intelligence; Biopsy; Breast neoplasms; Ultrasonography.