Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Eur Radiol. 2022 Dec;32(12):8238-8246. doi: 10.1007/s00330-022-08909-x. Epub 2022 Jun 15.
Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading.
A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists.
Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1-5 were considered negative and 6-10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1-9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%.
Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies.
• Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading • The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer.
人工智能（AI）在使用乳腺筛查的回顾性数据时显示出了有前景的结果。然而，很少有研究探讨在乳腺筛查中结合 AI 和放射科医生的不同策略的可能后果。
2009 年至 2018 年间，挪威乳腺筛查项目共对 122969 例数字筛查检查进行了回顾性处理，由一个 AI 系统对这些检查进行评分，从 1 到 10 分；1 分表示恶性肿瘤低怀疑度，10 分表示高怀疑度。结果与筛查结果信息合并，用于探索 11 种不同的 AI 与放射科医生结合场景下的共识、召回率和癌症检出率。
独立双读时的召回率为 3.2%，检出的癌症为 0.61%，间隔癌为 0.17%，作为参考值。在一种情况下，将 AI 评分 1-5 的检查视为阴性，将 6-10 分的检查进行标准独立双读，估计的召回率为 2.6%，检出的癌症为 0.60%。当评分 1-9 视为阴性，评分 10 分时进行双读，召回率为 1.2%，检出的癌症为 0.53%。在这两种情况下，如果为了共识而选择的间隔癌在筛查中被检出，潜在的检出癌症率可能高达 0.63%和 0.56%。在前一种情况下，乳腺筛查的阅读量将减少 50%，而后一种情况将减少 90%。
使用 AI 与放射科医生结合的几种理论场景有可能在不显著影响癌症检出率的情况下减少阅读量。在前瞻性研究中必须评估对召回率和间隔癌的可能影响。
使用 AI 与放射科医生结合的不同场景可以将阅读量减少 50%，并使检出癌症的比例在 0.59%到 0.60%之间，而标准独立双读的比例为 0.61%。
在乳腺筛查中使用 AI 与放射科医生结合有可能以高精度识别阴性筛查检查，并降低间隔癌的发生率。
