在CT肺癌筛查中使用人工智能进行结节和癌症检测的软件：测试准确性研究的系统评价
Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies
影响因子:7.70000
分区:医学1区 Top / 呼吸系统2区
发表日期:2024 Oct 16
作者:
Julia Geppert, Asra Asgharzadeh, Anna Brown, Chris Stinton, Emma J Helm, Surangi Jayakody, Daniel Todkill, Daniel Gallacher, Hesam Ghiasvand, Mubarak Patel, Peter Auguste, Alexander Tsertsvadze, Yen-Fu Chen, Amy Grove, Bethany Shinkins, Aileen Clarke, Sian Taylor-Phillips
摘要
为了检查人工智能（AI）软件辅助在肺癌筛查中的准确性和影响。使用CT的系统评价，对CE标记的基于AI的基于AI的软件，用于自动检测和分析CT肺癌筛查中的结节。从2012年到2023年3月，搜索了包括MEDLINE，EMBASE和COCHRANE CENTRAL在内的多个数据库。包括主要研究报告测试的准确性或对阅读时间或临床管理的影响。 Quadas-2和Quadas-C用于评估偏见的风险。我们进行了叙事综合。评估六个基于AI的软件并报告1970名患者的研究符合条件。所有这些都有偏见的高风险，并且有多个适用性问题。与遵守阅读相比，AI辅助阅读速度更快且通常提高了敏感性（ +5％至 +20％，用于检测/分类可操作的结节； +3％至 +15％，用于检测/分类恶性结节），具有较低特异性的较低特异性（-7％至未经cotity -noce crotisting/comporting conterutting/forcorable）均置于良好的效果/3％。没有恶性结节的人）。 AI援助倾向于增加分配给更高风险类别的结节的比例。假设癌症患病率为0.5％，这些结果将转化为150-750次癌症，每百万人参加筛查，但导致59 700至79 600人参加没有癌症的筛查的人接受不必要的CT监视。IAI在肺癌筛查中的援助可能会提高敏感性，但会提高假期效果和不必要的结果，并提高了不必要的结果。未来的研究需要通过改进的研究设计来提高AI辅助阅读的特异性，并最大程度地减少偏见和适用性问题的风险。CRD42021298449。
Abstract
To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis.Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (-7% to -3% for correctly detecting/categorising people without actionable nodules; -8% to -6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150-750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance.AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design.CRD42021298449.