Automated Diagnosis and Progression Rate of IPF Using HRCT

使用 HRCT 自动诊断 IPF 并确定其进展率

• 批准号: 9765383
• 负责人: Grace Hyun Jung Kim
• 金额: $ 11.32万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-16 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9765383
• 关键词: Acute; Air; Algorithms; Anatomy; Archives; Automation; Biological; Biopsy; Categories; Clinical; Clinical Data; Computer Analysis; Computer Assisted; Computer Vision Systems; Data; Data Element; Data Set; Data Sources; Deterioration; Development; Diagnosis; Diagnostic; Diffuse; Disease; Disease Progression; Disease stratification; Elderly; Etiology; Exhibits; General Population; Glass; Goals; Growth; High Resolution Computed Tomography; Image; Image Analysis; Impairment; Individual; Informatics; Interstitial Lung Diseases; Intraobserver Variability; Laboratories; Lobar; Lobe; Lung; Lung Transplantation; Lung diseases; Measurement; Measures; Methods; Modeling; Multicenter Trials; Natural History; Operative Surgical Procedures; Pathology; Patient Triage; Patients; Pattern; Pharmacologic Substance; Pharmacotherapy; Phenotype; Prevalence; Probability; Progression-Free Survivals; Progressive Disease; Public Health; Pulmonary Fibrosis; Registries; Reproducibility; Research; Risk; Role; Scanning; Spatial Distribution; Stable Disease; Standardization; Testing; Texture; Time; Time Management; Training; Transplantation; Visual; X-Ray Computed Tomography; base; clinical application; clinically relevant; cohort; computerized; data archive; digital imaging; disease natural history; disease phenotype; functional decline; idiopathic pulmonary fibrosis; image processing; imaging biomarker; improved; individual patient; individualized medicine; new therapeutic target; novel; novel therapeutics; predictive modeling; prognostic; programs; pulmonary function; quantitative imaging; respiratory; success; survival prediction; tool

Project Summary: Idiopathic pulmonary fibrosis (IPF) is a devastating disease of unknown etiology occurring in older adults. IPF is ultimately fatal with a median survival of 2 to 5 years, and exhibits a highly heterogeneous natural history. Broad categories of disease progression have been defined, but are not predictable at the time of diagnosis. Diagnosis and stratification of disease phenotypes are important in order to decipher the effects of novel therapies among individuals with biologically dissimilar natural histories and to better tailor therapy to individuals. Few computerized diagnostic tools have been developed for IPF that correlate with visual and surgical lung biopsy; most use clinical and functional variables independent of imaging findings. Prognostic determinants based on imaging features rely largely on subjective visual assessment of disease. In contrast, no good predictive models with localized region exist that anticipate the natural history of disease in advance of significant functional decline. Given the indispensable role of high resolution computed tomography (HRCT) in the diagnosis and surveillance of IPF, we propose to mine the rich information in HRCT data sets to develop robust, quantitative features that can anticipate disease progression in advance of debilitating respiratory compromise. We propose to use as a derivative dataset the anonymized clinical data and source images on 234 patients with IPF and 266 patients with IPF suspected, but not IPF based on HRCT and the surgical biopsy who have participated in multicenter trials, and whose data are archived at the UCLA Computer Vision and Imaging Biomarkers Laboratory. Using an image processing pipeline developed in our laboratory for high through-put quantitative image analysis, we will train a classifier with features of anatomic distribution and reproducible imaging features expressed with a quantitative lung fibrosis (QLF) score, testing on separate data from in an independent institutional registry of clinical and image data on patients with IPF seen in the UCLA Interstitial Lung Disease Program. Furthermore, the second aim is to develop a rate of progression at local region and to aggregate predictive models using Cox proportional regression models, which will be derived using only clinical covariates and combined clinical and imaging covariates, correlating these models with progression free survival. Our objectives are centered on the goals of using preexisting datasets to develop clinically meaningful models that diagnose and anticipate disease course in patients with IPF and subdividing patients into more homogeneous groups prior to the development of significant respiratory impairment. We anticipate that models can be used clinically at the individual patient level to enable more informed and timely management decisions to define more homogeneous cohorts for purposes of testing new targeted therapies and to better elucidate the effects of therapies in patients with biologically heterogeneous disease progression.

项目摘要： 特发性肺纤维化（IPF）是老年人发生的未知病因的毁灭性疾病。 IPF 最终是致命的，中位生存期为2至5年，并且表现出高度异质的自然历史。 已经定义了广泛的疾病进展，但在诊断时是无法预测的。 疾病表型的诊断和分层对于破译新型的影响很重要 具有生物学上不同自然史的个体中的疗法，并对个体更好地量身定制疗法。 很少为IPF开发与视觉和外科肺相关的IPF的计算机化诊断工具 活检；大多数使用临床和功能变量与成像发现无关。预后决定因素 基于成像特征很大程度上依赖于主观的疾病视觉评估。相反，没有好 存在具有局部区域的预测模型，可以预见疾病的自然历史 功能下降。鉴于高分辨率计算机断层扫描（HRCT）在 IPF的诊断和监视，我们建议在HRCT数据集中挖掘丰富的信息，以发展强大， 定量特征可以预见疾病在使呼吸损害损害之前的进展。 我们建议将用作234例患者的匿名临床数据和源图像作为衍生数据集 IPF和266名IPF患者涉嫌，但没有基于HRCT的IPF和具有外科活检 参加了多中心试验，其数据在UCLA计算机视觉和成像中存档 生物标志物实验室。使用在我们的实验室中开发的图像处理管道，以进行高贯穿 定量图像分析，我们将培训具有解剖分布和可重现特征的分类器 成像特征以定量肺纤维化（QLF）评分表示，对来自的单独数据进行测试 在UCLA间隙中看到的IPF患者的临床和图像数据的独立机构注册表 肺部病计划。此外，第二个目的是在当地和 使用COX比例回归模型的聚集预测模型，该模型将仅使用临床得出 协变量并将临床和成像协变量组合在一起，将这些模型与无进展的生存相关联。 我们的目标集中在使用先前存在的数据集开发临床上有意义的模型的目标上 诊断和预期IPF患者的疾病病程并将患者细分为更多 在发生重大呼吸障碍之前的均质组。我们预计该模型 可以在单个患者级别上临床使用以实现更明智和及时的管理决策 为了测试新的靶向疗法的目的定义更多均匀的队列并更好地阐明 疗法对生物学异质性疾病进展的患者的影响。

项目成果

Quantitative interstitial lung disease scores in idiopathic inflammatory myopathies: longitudinal changes and clinical implications.

特发性炎症性肌病的间质性肺疾病定量评分：纵向变化和临床意义。

• DOI: 10.1093/rheumatology/kead122
• 发表时间: 2023
• 期刊: Rheumatology (Oxford, England)
• 作者: Yeo,Jina;Yoon,SoonHo;Kim,JuYeon;Lee,JeongSeok;Lee,EunYoung;Goo,JinMo;Pourzand,Lila;Goldin,JonathanG;Kim,Grace-HyunJ;Ha,You-Jung
• 通讯作者: Ha,You-Jung