英国华威大学Sian Taylor-Phillips团队研究了在乳腺癌筛查计划中使用人工智能进行图像分析的准确性。该研究于2021年9月2日发表在《英国医学杂志》上。
为了评估人工智能（AI）在乳腺X线筛查实践中检测乳腺癌的准确性，研究组在Medline、Embase、Web of Science等大型数据库中检索2010年1月1日至2021年5月17日关于AI算法检测准确性的研究，筛选出单独或与放射科医生联合在女性数字乳房X光检查或测试中检测癌症的研究，并进行测试准确性研究的系统回顾。参考标准为组织学活检或随访（筛查阴性妇女）。研究结果包括检测准确性和检测到的癌症类型。由两名评审员独立评估纳入的文章并评估方法学质量。
研究组共纳入12项研究，涉及131822名筛查女性。未发现在筛查实践中评估AI检测准确性的前瞻性研究。研究方法质量较差。三项回顾性研究将AI系统与原始放射科医生的临床决定进行了比较，包括79910名女性，其中1878名在筛查后12个月内检出癌症或间隔期癌症。
在这些研究评估的36个AI系统中，34个（94%）的准确度低于单个放射科医生，并且所有系统的准确度都低于两个及以上放射科医生的一致意见。五项较小的研究（1086名女性，520名癌症患者）具有较高的偏倚风险和较低的临床通用性，报告称，所有五项评估的AI系统（作为替代放射科医生的独立系统或作为阅读辅助系统）都比单名放射科医生在实验室阅读检测结果更准确。在三项研究中，用于分诊的AI筛选出53%、45%和50%的低风险女性，但也筛选出了10%、4%和0%的放射科医生检测到的癌症。
研究结果表明，现有证据尚不能判断AI在乳腺癌筛查计划中的准确性，尚不清楚AI在临床途径中的哪些方面可能最为有利。AI系统的特异性不足以取代筛查计划中的放射科医生双读。
附：英文原文
Title: Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy
Author: Karoline Freeman, Julia Geppert, Chris Stinton, Daniel Todkill, Samantha Johnson, Aileen Clarke, Sian Taylor-Phillips
Issue&Volume: 2021/09/02
Abstract:
Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice.
Design Systematic review of test accuracy studies.
Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021.
Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected.
Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed.
Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists.
Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity.
DOI: 10.1136/bmj.n1872
Source: https://www.bmj.com/content/374/bmj.n1872
BMJ-British Medical Journal:《英国医学杂志》，创刊于1840年。隶属于BMJ出版集团，最新IF：93.333
官方网址：http://www.bmj.com/
投稿链接：https://mc.manuscriptcentral.com/bmj
本期文章：《英国医学杂志》：Online/在线发表
