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. 2022 Nov;29(6):967-977.
doi: 10.1007/s12282-022-01375-9. Epub 2022 Jun 28.

Impact of artificial intelligence in breast cancer screening with mammography

Lan-Anh Dang  1 , Emmanuel Chazard  2 , Edouard Poncelet  3 , Teodora Serb  3 , Aniela Rusu  3 , Xavier Pauwels  3 , Clémence Parsy  3 , Thibault Poclet  3 , Hugo Cauliez  3 , Constance Engelaere  3 , Guillaume Ramette  3 , Charlotte Brienne  3 , Sofiane Dujardin  3 , Nicolas Laurent  3
Affiliations

Impact of artificial intelligence in breast cancer screening with mammography

Lan-Anh Dang et al. Breast Cancer. 2022 Nov.

Abstract

Objectives: To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.

Methods: A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or "continuous BI-RADS 100". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.

Results: On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).

Conclusions: When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.

Keywords: Artificial intelligence; BI-RADS classification; Breast cancer; Mammography.

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Conflict of interest statement

The authors disclosed no relevant relationships.

Figures

Fig. 1
Fig. 1
Average ROC curve among readers NAR (yellow curve) and AR (green curve)
Fig. 2
Fig. 2
The AI score for this lesion was 6 meaning “indetermined characterization”; without AI, 7 out of 12 readers judged this exam as not suspicious, 2 readers assigned a BI-RADS 3 category, and 3 readers judged the lesion as suspicious for cancer. When reading with AI, one reader only judged the examinations as not suspicious, 2 readers assigned it with a BI-RADS 3 category while 9 readers suspected for cancer
Fig. 3
Fig. 3
Patient with a cancer on left breast; a mark by the expert; b mark by the AI; c architectural distortion only visible on tomosynthesis
See this image and copyright information in PMC

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