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

在美国，肺癌是主要的死亡原因。全国肺部筛查试验 (NLST)显示，通过低剂量CT筛查可以在早期发现更多的肺癌。 然而，对惰性肺癌和良性结节的过度诊断是 筛查，导致不必要的治疗、活组织检查、随访、辐射暴露增加、患者焦虑、 和成本。由于缺乏对结构图像特征与组织学的相关性的深入了解 肺结节的表现和缺乏准确疾病的有效诊断生物标志物 尽管对结节进行分类，目前对筛查发现的结节的诊断和管理仍然具有挑战性。 该项目的目标是开发一个基于量化的决策支持系统 应用先进的计算机视觉和组织病理学相关CT描述符(Q-PCD)诊断肺结节 机器学习技术表征结节的组织病理学特征并分析其 结合CT影像特征提高肺癌早期诊断率。我们假设 建议的Q-PCD分析将与组织病理学特征有很强的相关性，因此 将成为区分侵袭性、侵袭性前期和良性结节的更有效的生物标志物 传统的基于图像的特征或放射科医生的视觉判断。对结节的准确描述 类型将帮助放射科医生对所检测到的结节的管理作出决定；例如，允许及早 浸润性肺癌的发现和治疗、安全监测或以有限叶下叶取代肺叶切除术 切除浸润性前肺癌，保留良性结节的活检，从而降低发病率和 肺癌筛查项目的成本。我们的主要具体目标是：1)收集LDCT的大型数据库 筛选来自NLST项目和我所的病例以开发自动图像分析方法，2)到 开发一种基于肺结节定量病理相关CT描述符(Q-PCD)的新决策支持系统，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 由国家肺部筛查试验(NLST)和我所共同设定。收集的数据库将包括 提供的基线和后续扫描、病理数据、人口统计信息和其他信息 NLST。我们将开发自动分割方法来提取固体和子固体的体积 检测到的肺结节的成分，开发定量方法来表征放射学和 肺结节及其周围肺实质的病理特征，形成了一种新的 放射病理组学战略，将病理组学与放射组学联系起来，并确定新的成像生物标记物。我们会 开发一种具有结合图像和患者信息的联合生物标记物的临床可翻译的DSS，以及 评估其在肺癌诊断中的表现，包括其在基线筛查CT检查中的有效性 在后续的考试中。

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.