Latent class analysis in imaging studies as a method to estimate the true disease status based on multiple test results

研究问题：在影像学研究和临床实践中，潜在类别分析（LCA）是否是一种可行的替代面板诊断的方法？背景：诊断是具有挑战性的许多医疗条件，需要广泛的测试。然后，临床医生必须结合联合收割机的每一个测试结果，以确定疾病的状态，并建议正确的治疗。为了帮助决策，我们召集了一个临床医生小组，共同做出诊断。在评估新测试的研究中也是同样的情况-“小组诊断”作为比较新测试的参考，以确定准确性。然而，组织和进行小组诊断是费力的、资源密集的和主观的（Bertens等人，2013; Keating等人，2013年）。LCA是一种统计建模方法，结合多种测试结果来估计疾病状态。潜在类别模型（LCMs）比面板更快速，更便宜，更客观（MacLean & Dendukuri，2021）。也有专门针对诊断测试中LCA设计和报告研究的出版物（Cheung et al.，2021; Kostoulas等人，2017年）。然而，由于缺乏针对面板诊断的验证，吸收缓慢。将LCA转化为诊断测试将使诊断更有效，为患者，NHS和资助者带来实质性利益。目的：1）评估LCA是否，如何以及何时比已完成的研究中的面板更有效2）在正在进行的研究中前瞻性地应用和评估LCA 3）在临床实践中实施和评估LCM作为面板诊断的替代方法：该项目分为四个工作包：工作包1（从04/23至03/25）将评估LCM何时与六项由NIHR资助的诊断准确性研究中的既定面板诊断具有足够的一致性，以使研究人员能够信任该方法。（从04/25到06/26）将确定在什么情况下LCA可以可靠地估计疾病的患病率和测试的准确性，通过将其应用于模拟研究代表现实世界的情况。我们将把我们的发现纳入设计应用LCA的诊断准确性研究的指南。工作包3（从07/26到11/27）将设计数据收集，以评估LCA对正在进行的前列腺癌研究中的一个小组，调查MRI是否可以取代活检。我们将开发一种LCM，以在未来的子研究中构建参考标准。工作包4（从07/26到03/28）将比较LCA与正在进行的克罗恩病研究中的一个面板，该研究调查了一种用于检测疾病活动的新型MRI技术。我们将把常规临床数据纳入LCM中，在没有专家小组的情况下评估新的MRI技术，并探索LCM作为临床诊断工具的潜力。影响：鉴于该项目的环境，我非常适合在完成后立即在研究和临床环境中传播LCM，从而产生快速影响。我从三位放射学教授、一位诊断统计学教授和一位LCA方法的世界领导者那里获得了指导和支持。重要的是，我开发了这个项目，并将继续与IBD救济的患者合作，以确保输出是相关的和以患者为中心的。

Research question :Is latent class analysis (LCA) a viable alternative to panel diagnosis in imaging studies and clinical practice?Background:Diagnosis is challenging for many medical conditions, requiring extensive testing. Clinicians must then combine every test result to determine the disease status and recommend the correct treatment. To help decision-making,we assemble a panel of clinicians to arrive at a diagnosis together. It is the same situation in studies evaluating new tests - "panel diagnosis" acts as the reference to compare the new test against, to determine accuracy. However, organising and conducting panel diagnosis is laborious, resource-intensive, and subjective (Bertens et al., 2013; Keating et al., 2013). LCA is a statistical modelling method that combines multiple test results to estimate the disease status. Latent class models (LCMs) are more rapid, cheap, and objective than panels (MacLean & Dendukuri, 2021). There arealso publications on designing and reporting studies specifically for LCA in diagnostic testing (Cheung et al., 2021; Kostoulas et al., 2017). However, uptake is slow due to a lack of validations against panel diagnosis. Translating LCA into diagnostic testing will make diagnosis more efficient, incurring substantial benefits for patients, the NHS, and funders.Aims:1)Evaluate if, how, and when LCA is more effective than panels in completed studies2)Apply and evaluate LCA prospectively in ongoing studies3)Implement and evaluate LCMs in clinical practice as an alternative to panel diagnosisMethods:The project is organised into four work packages:Work Package 1 (from 04/23 to 03/25) will evaluate when LCMs have sufficient agreement with established panel diagnoses in six NIHR-funded diagnostic accuracy studies to enable researchers to trust the approach.Work Package 2 (from 04/25 to 06/26) will determine in what situations LCA can reliably estimate disease prevalence and test accuracy by applying it in simulated studies representative of real-world scenarios. We will incorporate our findings into guidelines for designing diagnostic accuracy studies that apply LCA.Work Package 3 (from 07/26 to 11/27) will design data collection to evaluate LCA against a panel in an ongoing prostate cancer study investigating whether MRI can replace biopsy. We will develop an LCM to construct a reference standard in future sub-studies.Work Package 4 (from 07/26 to 03/28) will compare LCA to a panel in an ongoing Crohn's disease study investigating a novel MRI technique for detecting disease activity. We will incorporate routine clinical data into the LCMs to evaluate new MRI techniques without an expert panel and to explore the potential of LCMs as diagnostic tools in a clinical setting.Impact:Given the environment for this project, I am exceptionally well-placed to disseminate LCMs in both research and clinical settings immediately following completion, leading to rapid impact. I secured mentoring and support from three Professors of Radiology, a Professor of Diagnostic Statistics, and a world-leader in LCA methods. Importantly, I developed this project and will continue to collaborate with patients at IBDrelief to ensure the output is pertinent and patient-centric.