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

期刊论文
发表日期：

2024年4月3日

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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

摘要

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.