AI 风险评分在乳腺癌诊断前的筛查乳房 X 光片中的应用。

AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis.

机构信息

From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.).

出版信息

Radiology. 2023 Oct;309(1):e230989. doi: 10.1148/radiol.230989.

DOI:10.1148/radiol.230989

PMID:37847135

Abstract

Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected ( = 1016) or interval ( = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers ( < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 See also the editorial by Mehta in this issue.

摘要

背景很少有研究评估人工智能（AI）在先前的筛查性乳房 X 线摄影中的作用。

目的检查 AI 风险评分在随后被诊断为乳腺癌的女性的筛查性乳房 X 线摄影中的分配情况。

材料与方法本回顾性研究使用了 2004 年 1 月至 2019 年 12 月期间作为挪威乳腺筛查项目一部分而进行的图像数据和筛查信息。随后对被诊断为癌症的女性的先前筛查检查分配了一个商用 AI 系统的 AI 风险评分（评分 1-7 表示恶性肿瘤风险低；8-9 表示中度风险；10 表示恶性肿瘤风险高）。还根据 AI 评分评估了癌症的乳房 X 线摄影特征。使用双变量检验检验 AI 评分与乳房 X 线摄影特征之间的关联。

结果在总共 1602 名女性（平均年龄 59 岁 ±5.1 [标准差]）的 2787 次先前筛查检查中，在诊断前的筛查轮次中，389（38.3%）和 231（39.4%）例癌症的乳房 X 线摄影中，AI 风险评分为 10。在有 AI 评分的可检测癌症中，在诊断前两个筛查轮次（4 年）中，23.0%（531 例中有 122 例）的评分是 10。在可检测到的浸润性癌症中，AI 评分与乳房 X 线摄影特征相关（ <.001）。在评分 10 的 317 例可检测病例中，有 13.6%（43 例）登记有钙化伴密度，而评分 1-7 的 322 例病例中则有 4.6%（15 例）。

结论在先前的筛查中，超过三分之一的可检测到的癌症和间隔性癌症具有最高的 AI 风险评分，这表明在乳房 X 线摄影筛查中使用 AI 可能会更早地发现乳腺癌。