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Radiology, Jun 2025, 315 (3) | https://doi.org/10.1148/radiol.242408
Invasive Breast Cancers Missed by AI Screening of Mammograms
Ok Hee Woo1, Sung Eun Song2, Su Jin Choe3, Minhye Kim1, Kyu Ran Cho2, Bo Kyoung Seo4
1Department of Radiology, Korea University Guro Hospital, Seoul, Korea
2Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea
3Korea University College of Medicine, Seoul, Korea
4Department of Radiology, Korea University Ansan Hospital, Ansan, Korea
Address correspondence to: S.E.S.
1Department of Radiology, Korea University Guro Hospital, Seoul, Korea
2Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea
3Korea University College of Medicine, Seoul, Korea
4Department of Radiology, Korea University Ansan Hospital, Ansan, Korea
Address correspondence to: S.E.S.
Abstract
Background
Little is known about the features of invasive breast cancers missed by artificial intelligence (AI) on mammograms.
Purpose
To assess the false-negative rate (FNR) of AI mammogram evaluation according to molecular subtype and to investigate the features of and reasons for AI-missed cancers.
Materials and Methods
This retrospective study identified consecutive patients diagnosed with breast cancer between January 2014 and December 2020. Commercial AI software was used to read the mammograms, and abnormality score (AS) was acquired. AI-missed cancers were defined as those for which AI did not identify a precise location matching the reference standard. The FNR was calculated by counting AI-missed cancers according to molecular subtype (hormone receptor–positive [luminal] vs human epidermal growth factor receptor 2 [HER2]–enriched vs triple-negative). Three blinded radiologists classified AI-missed cancers as either actionable or under threshold, and reasons for misses were determined through nonblinded reviews. Features were compared according to AI detection with the χ2 test.
Results
A total of 1082 consecutive women diagnosed with 1097 cancers (mean age, 54.3 years ± 11 [SD]) were included. AI missed 14% (154 of 1097) of cancers. The FNR was lowest in the HER2-enriched subtype (9% [36 of 398] in the HER2-enriched subtype, 17.2% [106 of 616] in the luminal subtype, and 14.5% [12 of 83] in the triple-negative subtype; P = .001). Compared with AI-detected cancers, AI-missed cancers were associated with younger age, a tumor size less than or equal to 2 cm, a lower histologic grade, fewer lymph node metastases, more Breast Imaging Reporting and Data System category 4 findings, lower Ki-67 expression, and nonmammary zone locations (all, P < .05). In blinded reviews, 61.7% (95 of 154) of AI-missed cancers were actionable; the reasons for misses were dense breasts (n = 56), nonmammary zone locations (n = 22), architectural distortions (n = 12), and amorphous microcalcifications (n = 5).
Conclusion
To reduce AI-missed cancers on mammograms, attention should be given to luminal cancer, dense breasts, nonmammary zone locations, architectural distortions, and amorphous calcifications.
Little is known about the features of invasive breast cancers missed by artificial intelligence (AI) on mammograms.
Purpose
To assess the false-negative rate (FNR) of AI mammogram evaluation according to molecular subtype and to investigate the features of and reasons for AI-missed cancers.
Materials and Methods
This retrospective study identified consecutive patients diagnosed with breast cancer between January 2014 and December 2020. Commercial AI software was used to read the mammograms, and abnormality score (AS) was acquired. AI-missed cancers were defined as those for which AI did not identify a precise location matching the reference standard. The FNR was calculated by counting AI-missed cancers according to molecular subtype (hormone receptor–positive [luminal] vs human epidermal growth factor receptor 2 [HER2]–enriched vs triple-negative). Three blinded radiologists classified AI-missed cancers as either actionable or under threshold, and reasons for misses were determined through nonblinded reviews. Features were compared according to AI detection with the χ2 test.
Results
A total of 1082 consecutive women diagnosed with 1097 cancers (mean age, 54.3 years ± 11 [SD]) were included. AI missed 14% (154 of 1097) of cancers. The FNR was lowest in the HER2-enriched subtype (9% [36 of 398] in the HER2-enriched subtype, 17.2% [106 of 616] in the luminal subtype, and 14.5% [12 of 83] in the triple-negative subtype; P = .001). Compared with AI-detected cancers, AI-missed cancers were associated with younger age, a tumor size less than or equal to 2 cm, a lower histologic grade, fewer lymph node metastases, more Breast Imaging Reporting and Data System category 4 findings, lower Ki-67 expression, and nonmammary zone locations (all, P < .05). In blinded reviews, 61.7% (95 of 154) of AI-missed cancers were actionable; the reasons for misses were dense breasts (n = 56), nonmammary zone locations (n = 22), architectural distortions (n = 12), and amorphous microcalcifications (n = 5).
Conclusion
To reduce AI-missed cancers on mammograms, attention should be given to luminal cancer, dense breasts, nonmammary zone locations, architectural distortions, and amorphous calcifications.
논문정보
- Research article
- 2025년 06월 (BRIC 등록일 2025-06-25)
- 국내 연구진
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