■肺癌是英国最常见的癌症类型之一。它经常被诊断为晚期。肺癌的5年生存率低于10%。早期诊断可以提高生存率。具有人工智能开发的算法的软件可能有助于识别可疑的肺癌。
■本综述旨在确定辅助人工智能软件的证据,用于分析可疑肺癌的胸部X光片,并制定一个概念性的成本效益模型,以告知讨论为未来的经济评估制定一个完全可执行的成本效益模型需要什么。
■数据源为MEDLINEAll,EMBASE,Cochrane系统评价数据库,科克伦中部,认识论,ACM数字图书馆,世界卫生组织国际临床试验注册平台,临床专家,塔夫茨成本效益分析登记处,公司意见书和临床专家。搜索时间为2022年11月25日至2023年1月18日。
■采用快速证据合成方法。公司的数据受到了审查。资格标准是(1)由于提示肺癌的症状或与肺癌无关的原因而进行胸部X线检查的初级保健人群;(2)将放射科专家评估胸部X线与辅助人工智能软件与仅放射科专家进行比较的研究设计,以及(3)与测试准确性有关的结果,使用人工智能软件和患者相关结果的实际意义。开发了概念性决策分析模型,以告知辅助人工智能软件的潜在全面成本效益评估,用于分析胸部X射线图像以识别可疑肺癌。
■搜索中确定的或公司提交的研究均不符合审查的纳入标准。来自六项不符合纳入标准的研究的上下文信息提供了一些证据,表明放射学专家结合人工智能软件对胸部X射线进行解释时,肺癌检测(但不是结节检测)的敏感性可能比由放射学专家单独解释时更高。没有观察到显著差异的特异性,阳性预测值或检测到的癌症数量。六项研究均未提供有关辅助人工智能软件临床有效性的证据。概念模型强调了诊断途径过程中输入数据的匮乏,并确定了证据链接所需的关键假设。
■这篇综述采用了快速证据合成方法。这只包括一名审查人员进行审查的所有要素,和仅以英语进行的有针对性的搜索。没有确定合格的研究。
■目前没有证据适用于本综述,即使用辅助人工智能软件在胸部X线片上检测疑似肺癌,无论是从初级保健机构转诊的有肺癌症状的人还是因其他原因从初级保健机构转诊的人。
■未来的研究需要了解辅助人工智能软件检测肺结节和癌症的准确性,以及它对临床决策和患者预后的影响。为概念模型生成关键输入参数的研究将能够对模型结构进行细化,并转换为完整的工作模型,分析人工智能软件对此适应症的成本效益。
■本研究注册为PROSPEROCRD42023384164。
■该奖项由美国国家卫生与护理研究所(NIHR)证据综合计划(NIHR奖项参考:NIHR135755)资助,并在《卫生技术评估》中全文发表;卷。28号50.有关更多奖项信息,请参阅NIHR资助和奖励网站。
肺癌是英国最常见的癌症之一。早期诊断可以提高生存率,因为肺癌通常被诊断为晚期。胸部X射线可用于识别肺癌的特征。X射线可能会延迟,有时在他们身上看不到肺癌的特征。人工智能软件可以通过在胸部X光片上发现癌症的特征并突出显示它们来提供帮助。放射科医生将从软件中查看X射线和信息。缺乏有关如果使用人工智能软件,肺癌诊断将如何改变的信息,以及国家卫生服务的成本可能是多少。该项目研究了人工智能软件在初级保健人员中检测肺癌的应用。软件公司被邀请提供证据。没有研究在初级保健人群中研究这个话题。我们总结了我们能找到的最接近的证据。所有这些都有缺陷,因此,我们无法判断结果是否准确或有助于本次审查。目前尚不清楚人工智能是否有助于发现癌症或改善人们的健康。我们建立了一个理论模型来讨论评估人工智能软件在检测肺癌方面是否具有成本效益的最佳方法,以及在一个完全有效的模型中需要什么证据来做到这一点。5家公司提供的成本和替代定价模型被用来计算增加人工智能软件的成本,以审查从他们的全科医生推荐的人的胸部X光,前5年,基于一个国家卫生服务信托。需要未来的研究来确定辅助人工智能对测试准确性的影响,临床决策和患者结局(例如死亡率和发病率)。
UNASSIGNED: Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer.
UNASSIGNED: This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation.
UNASSIGNED: The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023.
UNASSIGNED: Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer.
UNASSIGNED: None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage.
UNASSIGNED: This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified.
UNASSIGNED: There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons.
UNASSIGNED: Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication.
UNASSIGNED: This study is registered as PROSPERO CRD42023384164.
UNASSIGNED: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.
Lung cancer is one of the most common types of cancer in the United Kingdom. Early diagnosis may improve survival, as lung cancer is often diagnosed late. Chest X-rays can be used to identify features of lung cancer. There can be delays in getting X-rays, and sometimes features of lung cancer are not seen on them. Artificial intelligence software may help by finding features of cancer on chest X-rays and highlighting them. A radiologist will look at the X-rays and information from the software. There is a lack of information about how lung cancer diagnosis could change if artificial intelligence software is used and what the costs may be to the National Health Service. This project looked at the use of artificial intelligence software in the detection of lung cancer in people referred from primary care. Software companies were invited to provide evidence. There were no studies that looked at this topic among people from primary care. We summarised the closest evidence we could find instead. All of this had flaws, so we could not tell if the results were accurate or helpful to this review. It was not clear if artificial intelligence helped to find cancers or improve people’s health. We made a theoretical model to discuss the best way to assess if artificial intelligence software might be cost-effective in detecting lung cancer and what evidence would be needed to do this in a fully working model. Costs and alternative pricing models provided by five companies were used to calculate the cost of adding artificial intelligence software to review chest X-rays in people referred from their general practitioner, for the first 5 years, based on one National Health Service trust. Future studies are needed to identify the impact of adjunct artificial intelligence on test accuracy, clinical decision-making and patient outcomes (e.g. mortality and morbidity).