Computer-aided CT imaging and integration with molecular endotyping to stratify fibrotic lung disease

背景：间质性肺病(ILDS)在英国每年造成约7000人死亡，其中特发性肺纤维化(IPF)是最常见的诊断。ILDS的分类是基于病因、高分辨率(HR)CT扫描表现和肺组织学，这需要手术活检。这是有问题的，原因有很多，即：1.严重依赖肺活检，这是一种具有2-7%死亡率的侵入性手术，许多患者不会经历，导致诊断为“无法分类的疾病”；2.现有方法不能可靠地提供预后或治疗效果的信息；3.CT的临床报道是主观的，不是定量的。肺纤维化的特点是缺乏明确的工具来实现诊断的准确性，导致疾病实体的高度异质性。最近的工作：CT纹理分析平台，如自适应多特征方法(AMFM)和计算机辅助病理评估和评级的肺信息学(CALPER)，先前已在临床环境中应用研究。然而，这些还没有在已知基本事实(存活率、住院时间、肺功能下降率、治疗反应)的纵向队列中得到验证。在过去的几年里，在肺纤维化的诊断和预后生物标记物和基因图谱方面取得了进展。其中一些已经在几个患者队列中得到了验证。然而，这些研究中的绝大多数都局限于IPF。此外，最大的研究是基于招募到临床试验中的患者，而不是“真实世界”的受试者。资源：数据可从已建立的基因库、生物库和图像库获得，以及一个独特的、经伦理批准的、预期填充的数据库，旨在描述肺纤维化的自然历史。队列包括自2002年以来连续出现1100名同意的肺纤维化患者，其中不到1%的患者失去了随访。所有患者都进行了CT扫描，800多名患者进行了连续扫描。CT扫描托管在苏格兰国家服务中心(National Services Scotland，NSS)内，这与位于爱丁堡Farr节点的FARR网络位于同一地点，为成像、临床和‘基因组数据’提供了一个安全的分析环境。此外，队列中的大多数受试者都可以获得血清和基因组DNA样本，以及包括根据临床、CT、活检类别、连续肺功能的疾病表型在内的变量的完整数据集。目的：整合已知和新颖的生物标志物、基因多态和定量CT成像(放射基因组学)，以便通过机器学习方法有效地询问这些数据，以确定具有临床意义的疾病簇。目的是确定同质亚组，更好地定义患者的预后和治疗反应。初步工作计划：1.确定可以有效区分进展者和非进展者的血清生物标志物。基因分型：对Gwas和RNA-seq数据集进行分析。使用卡尺纹理分析平台进行定量CT分析。在我们的数据集上验证平台。制定和测试用于将扫描分类为诊断组的交互协议。整合和询问分子、影像和表型数据，以便对疾病分层进行分析。最终，将开发一种自动化和辅助工具，基于一组不同的预定义变量，对肺纤维化的诊断、预后、下降率和治疗反应进行个性化预测。

Background:Interstitial lung diseases (ILDs) account for approximately 7000 deaths in the UK every year, with idiopathic pulmonary fibrosis (IPF) identified as the most common diagnosis. Classification of ILDs is based upon causation, high resolution (HR) CT scan appearance and lung histology, which requires surgical biopsy.This is problematic for numerous reasons, namely: 1. There is strong reliance on lung biopsy, an invasive procedure with a 2-7% mortality that many patients will not undergo, leading to a diagnosis of "unclassifiable disease"; 2. The current methods do not reliably inform of either prognosis or treatment efficacy; 3. Clinical reporting of CT's is subjective and not quantitative.Lung fibrosis is notable for lacking definitive tools to achieve diagnostic precision, resulting in highly heterogenous disease entities.Recent work:CT texture analysis platforms, such as the Adaptive Multiple Features Method (AMFM) and the Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER), have previously been studied with applications in clinical settings. However, these have not been validated in longitudinal cohorts in which ground truth (survival, time to hospitalisation, rate of decline in lung function, response to treatment) is known.Over the past few years, advances in diagnostic and prognostic biomarker and genetic profiling in lung fibrosis have been made. Some of these have been validated in several cohorts of patients. However, the vast majority of these studies are confined to IPF. Additionally, the largest studies are based on patients recruited to clinical trials and not 'real-world' subjects.Resources:Data is available from established gene-, bio- and image-banks, and a unique, ethically approved, prospectively populated database designed to depict the natural history of lung fibrosis. The cohort consists of >1100 consecutively presenting consented patients with lung fibrosis since 2002, with less than 1% lost to follow-up. All patients have CT scans, and more than 800 patients have serial scans. CT scans are hosted within National Services Scotland (NSS) and this is co-located with the Farr network in the Edinburgh Farr node, enabling a safe haven analytic environment for imaging, clinical and 'omic data'.Furthermore, serum and genomic DNA samples are available from the majority of subjects from the cohort, along with a complete dataset of variables including disease phenotype according to clinical-, CT-, biopsy-category, serial lung function.Aims:To integrate known and novel biomarkers, genetic polymorphisms and quantitative CT imaging (radiogenomics) such that these data can be effectively interrogated through machine learning approaches to define clinically meaningful clusters of disease. The aim is to determine homogenous subgroups that better define patient prognosis and response to therapy.Preliminary programme of work:1. Identify serum biomarkers, which may effectively discriminate between progressors and non-progressors.2. Genotyping: perform analysis of GWAS and RNA-seq datasets.3. Quantitative CT analysis with the CALIPER texture analysis platform. Validate the platform on our datasets. Develop and test an interactive protocol for classification of scans into diagnostic groups.4. Integration and interrogation of molecular, imaging and phenotypical data such that analyses can be performed for the stratification of disease.Ultimately, an automated and assistive tool would be developed for personalised predictions of diagnosis, prognosis, rate of decline and response to treatment in lung fibrosis, based on a diverse set of pre-defined variables.