人工智能癌症检测软件可以将某些乳腺钼靶筛查影像分为三类：不需放射科医师复核、需要放射科医师复核、需要进一步检查。进一步检查的目的是模拟女性选择进行更敏感的筛查，从而促进早期发现乳腺癌，否则将成为间隔期乳腺癌或下轮筛查检出乳腺癌。
2020年8月26日，英国《柳叶刀》数字化医疗分册在线发表瑞典卡罗林学院、圣戈兰医院、卡罗林大学医院、皇家理工学院、生命科学实验中心的研究报告，探讨了人工智能如何减少放射科医师的工作量并增加乳腺癌检出率。
该回顾模拟研究对2008～2016年参加瑞典斯德哥尔摩省乳腺癌筛查的7364例女性进行回顾分析，其中连续参加两次筛查的全部乳腺癌女性547例、随机抽取体重增加的健康女性对照者6817例（此类女性每个筛查间隔期新发乳腺癌比例为0.7%）。根据商品化人工智能癌症检测软件的预测评分，对漏诊和额外检出的乳腺癌进行分析。
结果，对60%、70%或80%的人工智能评分最低、被归入不需放射科医师复核的女性进行复核，筛查漏诊的乳腺癌比例为0、0.3%（95%置信区间：0.0～4.3）或2.6%（95%置信区间：1.1～5.4）。
对1%或5%的人工智能评分最高、被归入需要进一步检查的女性进行复核，可能额外检出的乳腺癌分别为200例间隔期乳腺癌增加24例（12%）或53例（27%）、347例下轮筛查检出乳腺癌增加48例（14%）或121例（35%）。
因此，该研究结果表明，利用商品化人工智能癌症检测软件将乳腺钼靶筛查影像归类为不需放射科医师复核、需要进一步检查，可能将放射科医师的工作量减少一半以上，并额外检出相当一部分乳腺癌，否则可能到以后才被诊断。
Lancet Digit Health. 2020 Aug 26;2(9):e468-e474.
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study.
Karin Dembrower, Erik Wahlin, Yue Liu, Mattie Salim, Kevin Smith, Peter Lindholm, Martin Eklund, Fredrik Strand.
Karolinska Institute, Stockholm, Sweden; Capio Sankt Gorans Hospital, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden; KTH Royal Institute of Technology and Science for Life Laboratory, Stockholm, Sweden.
BACKGROUND: We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection.
METHODS: In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0.7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer.
FINDINGS: 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0.3% (95% CI 0.0-4.3), or 2.6% (1.1-5.4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively.
INTERPRETATION: Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later.
FUNDING: Stockholm City Council.
DOI: 10.1016/S2589-7500(20)30185-0
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