人工智能筛查乳房x光检查遗漏的浸润性乳腺癌

IF 12.1 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiology Pub Date : 2025-06-01 DOI:10.1148/radiol.242408
Ok Hee Woo, Sung Eun Song, Su Jin Choe, Minhye Kim, Kyu Ran Cho, Bo Kyoung Seo
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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 χ<sup>2</sup> 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; <i>P</i> = .001). 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引用次数: 0

摘要

人工智能(AI)在乳房x光检查中遗漏的浸润性乳腺癌特征尚不清楚。目的根据分子亚型评估人工智能乳房x线检查的假阴性率(FNR),探讨人工智能漏诊肿瘤的特点及原因。材料和方法本回顾性研究确定了2014年1月至2020年12月期间连续诊断为乳腺癌的患者。使用商业人工智能软件读取乳房x光片,获取异常评分(AS)。人工智能遗漏的癌症被定义为那些人工智能没有确定符合参考标准的精确位置的癌症。根据分子亚型(激素受体阳性[luminal] vs人表皮生长因子受体2 [HER2]富集vs三阴性)对ai遗漏的癌症进行计数,计算FNR。三名盲法放射科医生将人工智能漏诊的癌症分类为可操作或低于阈值,并通过非盲法评价确定漏诊的原因。采用χ2检验比较人工智能检测的特征。结果共纳入1082例连续诊断为1097例癌症的女性(平均年龄54.3岁±11 [SD])。人工智能漏诊了14%(1097例中的154例)的癌症。FNR在her2富集亚型中最低(her2富集亚型为9%(398例中36例),luminal亚型为17.2%(616例中106例),三阴性亚型为14.5%(83例中12例);P = .001)。与人工智能检测到的癌症相比,人工智能遗漏的癌症与年龄更小、肿瘤大小小于或等于2厘米、组织学分级更低、淋巴结转移更少、更多的乳腺影像学报告和数据系统4类发现、更低的Ki-67表达和非乳腺区位置相关(均P < 0.05)。在盲法评价中,人工智能遗漏的癌症中有61.7%(154例中的95例)是可采取行动的;漏诊的原因包括致密乳房(n = 56)、非乳腺区位置(n = 22)、结构扭曲(n = 12)和无定形微钙化(n = 5)。结论为了减少人工智能在乳房x线检查中的漏诊,应注意腔内癌、致密乳房、非乳腺区位置、结构扭曲和无定形钙化。在CC BY 4.0许可下发布。本文有补充材料。参见本期马伦的社论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Invasive Breast Cancers Missed by AI Screening of Mammograms.

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. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mullen in this issue.

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来源期刊
Radiology
Radiology 医学-核医学
CiteScore
35.20
自引率
3.00%
发文量
596
审稿时长
3.6 months
期刊介绍: Published regularly since 1923 by the Radiological Society of North America (RSNA), Radiology has long been recognized as the authoritative reference for the most current, clinically relevant and highest quality research in the field of radiology. Each month the journal publishes approximately 240 pages of peer-reviewed original research, authoritative reviews, well-balanced commentary on significant articles, and expert opinion on new techniques and technologies. Radiology publishes cutting edge and impactful imaging research articles in radiology and medical imaging in order to help improve human health.
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