Histopathology correlated quantitative analysis of lung nodules with LDCT for early detection of lung cancer

肺结节的组织病理学相关定量分析与 LDCT 早期发现肺癌

• 批准号: 10164728
• 负责人: CHUAN ZHOU
• 金额: $ 45.58万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10164728
• 关键词: Advocate; Anxiety; Archives; Benign; Biological Markers; Biopsy; Cause of Death; Characteristics; Collection; Computer Vision Systems; Data; Database Management Systems; Databases; Decision Aid; Decision Making; Decision Support Systems; Descriptor; Development; Diagnosis; Disease; Early Diagnosis; Early treatment; Effectiveness; Evaluation; Excision; Foundations; Goals; Health Care Costs; Histologic; Histopathology; Image; Individual; Indolent; Institutes; Joints; Judgment; Knowledge; Lobectomy; Lung; Lung nodule; Machine Learning; Malignant neoplasm of lung; Maps; Methods; Morbidity - disease rate; Nodule; Pathologic; Pathology; Patient risk; Patients; Performance; Property; Radiation exposure; Radiology Specialty; Reference Standards; Reporting; Reproducibility; Risk Factors; Scanning; Screening Result; Solid; Structure; Structure of parenchyma of lung; Techniques; Testing; Thoracic Radiography; United States; Visual; Woman; X-Ray Computed Tomography; attenuation; automated image analysis; automated segmentation; base; cancer diagnosis; clinically translatable; computed tomography screening; cost; diagnostic biomarker; effectiveness validation; follow-up; high risk; imaging biomarker; improved; large datasets; low dose computed tomography; lung cancer screening; men; mortality; novel; pathology imaging; personalized medicine; radiologist; radiomics; screening; screening program; treatment optimization; treatment strategy; unnecessary treatment

Lung cancer is a leading cause of death in the United States. The National Lung Screening Trial (NLST) showed that more lung cancers can be detected at an early stage with low dose CT screening. However, over-diagnosis of indolent lung cancer and benign nodules is one of the major limitations of screening, resulting in unnecessary treatment, biopsy, follow-up, increased radiation exposure, patient anxiety, and cost. Due to a lack of in-depth knowledge of the correlation of structural image features and histologic findings of lung nodules and the absence of validated diagnostic biomarkers for accurate disease categorization, the current diagnosis and management of the screen-detected nodules remains challenging. The goal of this proposed project is to develop a decision support system (DSS) based on quantitative histopathology correlated CT descriptor (q-PCD) of pulmonary nodules using advanced computer vision and machine learning techniques to characterize the histopathologic features of nodules and analyze their correlations with CT image features for improvement of early detection of lung cancer. We hypothesize that the proposed q-PCD analysis will have strong association with histopathologic characterization, and therefore will be a more effective biomarker for differentiation of invasive, pre-invasive, and benign nodules than conventional image-based features or radiologists' visual judgement. Accurate characterization of the nodule types will assist radiologists in making decision for management of the detected nodules; e.g., enabling early detection and treatment of invasive lung cancer, safe surveillance or replacing lobectomy with limited sublobar resection for pre-invasive lung cancer, and sparing biopsy of benign nodules, thereby reducing morbidity and costs in lung cancer screening programs. Our major specific aims are to 1) collect a large database of LDCT screening cases from NLST project and our institute to develop automated image analysis methods, 2) to develop a new DSS based on quantitative pathologic correlated CT descriptors (q-PCD) of lung nodules, 3) validate the effectiveness of DSS in lung cancer diagnosis. To achieve these aims, we will collect a large data set from the National Lung Screening Trial (NLST) and our institute. The collected database will include the baseline and follow up scans, pathology data, demographic information and other information provided by NLST. We will develop automated segmentation methods to extract the volumes of the solid and sub-solid components of detected lung nodules, develop quantitative methods to characterize the radiologic and pathologic features of lung nodules as well as the surrounding lung parenchyma, develop a novel radiopathomics strategy to correlate pathomics with radiomics, and to identify new imaging biomarkers. We will develop a clinically-translatable DSS with a joint biomarker combining both image and patient information, and evaluate its performance in lung cancer diagnosis, including its effectiveness in baseline screening CT exams and in follow up exams.

肺癌是美国的主要死亡原因。国家肺筛查试验 （NLST）显示，低剂量CT筛查可以在早期检测到更多的肺癌。 然而，对惰性肺癌和良性结节的过度诊断是其主要局限性之一。 筛查，导致不必要的治疗，活检，随访，增加辐射暴露，患者焦虑， 和成本由于缺乏对结构图像特征和组织学特征的相关性的深入了解， 肺结节的发现和缺乏准确疾病的有效诊断生物标志物 尽管目前的诊断和管理方法与分类方法不同，但筛查检测到的结节的当前诊断和管理仍然具有挑战性。 本项目的目标是开发一个基于定量的决策支持系统（DSS）， 使用先进计算机视觉的肺结节的组织病理学相关CT描述符（q-PCD） 机器学习技术来表征结节的组织病理学特征，并分析其 与CT图像特征的相关性，以提高肺癌的早期发现。我们假设 拟定的q-PCD分析将与组织病理学表征密切相关，因此 将是一个更有效的生物标志物，用于区分浸润性，浸润前和良性结节， 常规的基于图像的特征或放射科医师的视觉判断。准确描述结核特征 类型将帮助放射科医师做出对检测到的结节的管理的决定;例如，早期启用 浸润性肺癌的检测和治疗，安全监测或以有限肺叶切除术取代肺叶切除术 切除浸润前肺癌，保留良性结节活检，从而降低发病率， 肺癌筛查项目的成本。我们的主要具体目标是：1）收集大量LDCT数据库 从NLST项目和我们的研究所筛选病例，以开发自动化图像分析方法，2） 基于肺结节的定量病理相关CT描述符（q-PCD）开发新的DSS，3） 验证DSS在肺癌诊断中的有效性。为了实现这些目标，我们将收集大量数据 国家肺筛查试验（NLST）和我们的研究所。收集的数据库将包括 基线和随访扫描、病理学数据、人口统计学信息和其他信息 NLST。我们将开发自动分割方法来提取固体和亚固体的体积 检测到的肺结节的组成部分，开发定量方法来表征放射学和 肺结节的病理特征以及周围的肺实质，开发了一种新的 放射病理组学策略，将病理组学与放射组学相关联，并鉴定新的成像生物标志物。我们将 开发具有结合图像和患者信息的联合生物标志物的临床可翻译DSS，以及 评估其在肺癌诊断中的性能，包括其在基线筛查CT检查中的有效性 以及后续检查。