Macro-vasculature: A Novel Image Biomarker of Lung Cance

大血管系统：肺癌的新型图像生物标志物

• 批准号: 10311064
• 负责人: Jiantao Pu
• 金额: $ 63.21万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-13 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10311064
• 关键词: Administrator; Adult; Age; Benign; Biological Markers; Biopsy; Blood Vessels; Breast; Cancer Etiology; Cause of Death; Cessation of life; Characteristics; Chest; Classification; Clinical; Clinical Management; Colon; Computational algorithm; Data Set; Development; Discrimination; Early Diagnosis; Ensure; Environment; Ethnic Origin; Funding; Gender; Goals; Grant; Growth; Image; Individual; Institution; Investigation; Lead; Lung; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Modeling; Morphology; Nodule; Odds Ratio; Outcome; Output; Participant; Patients; Pattern; Performance; Policies; Positioning Attribute; Positron-Emission Tomography; Predictive Value; Procedures; Property; Protocols documentation; Publishing; Radiation exposure; Recording of previous events; Reporting; Risk Assessment; Role; Sampling; Scanning; Signal Transduction; Smoker; Smoking History; Spiculate; Statistical Data Interpretation; Symptoms; Technology; Testing; Texture; Thoracic Radiography; Time; Tobacco smoking behavior; Tumor Angiogenesis; United States; United States Centers for Medicare and Medicaid Services; United States National Institutes of Health; Unnecessary Procedures; Variant; Work; X-Ray Computed Tomography; base; cancer biomarkers; cancer diagnosis; cancer therapy; chest computed tomography; computed tomography screening; cost; demographics; density; follow-up; imaging biomarker; improved; longitudinal database; low dose computed tomography; lung cancer screening; mortality; multidisciplinary; novel; novel marker; pulmonary function; radiologist; screening; spatial relationship; success; tumor; tumor growth

ABSTRACT Lung cancer remains the leading cause of cancer related deaths in the United States and worldwide. The high mortality associated with lung cancer is in part due to underutilization of and limited access to lung cancer screening that impedes early diagnosis. The apprehension of some clinicians and policymakers towards lung cancer screening with low-dose computed tomography (LDCT) exams is based on concerns of lead-time bias and high false-positive rate. The development of a robust lung cancer biomarker that reduces screen-detected false positives and improves classification of indeterminate nodules would relieve some of the concerns related to lung cancer screening. Although investigations show that screening with LDCT scans may reduce lung cancer mortality by 20% compared to chest x-ray, it is reported that ~96% of suspicious findings (mostly indeterminate nodules) turn out to be non-cancerous (false positives). Clinical management of screen-detected indeterminate nodules often leads to unnecessary, costly, and potentially harmful follow-up procedures (e.g., follow-up CT scan, positron emission tomography (PET)/CT exam, invasive biopsies). We have developed an exciting and novel image-based macro-vasculature feature to discriminate benign from malignant nodules. We propose to further develop the feature and validate it across a range of CT protocols and scans from other institutions. We will also integrate the macro-vasculature features with clinical information (e.g., age, gender, smoking history, lung function) and evaluate the model's ability to discriminate benign from malignant screen-detected indeterminate nodules. We will investigate if a radiologist's classification of indeterminate nodules improves with the output of the integrative model compared to classification without the integrative model. The success of this project may lead to a novel and robust lung cancer biomarker to accurately assess screen-detected indeterminate nodules that can significantly reduce the number of unnecessary follow-up procedures during lung cancer screening.

抽象的 肺癌仍然是美国和全世界癌症相关死亡的主要原因。这 与肺癌相关的高死亡率部分是由于肺癌的利用不足和获取机会有限 妨碍早期诊断的筛查。一些临床医生和政策制定者对肺病的担忧 使用低剂量计算机断层扫描 (LDCT) 检查进行癌症筛查是基于对交付时间偏差的担忧 且假阳性率高。开发一种强大的肺癌生物标志物，可减少筛查检测 误报和改进不确定结节的分类将减轻一些相关的担忧 到肺癌筛查。尽管研究表明 LDCT 扫描筛查可能会减少肺癌 与胸部 X 光检查相比，死亡率降低 20%，据报道，约 96% 的可疑发现（大部分是不确定的） 结节）被证明是非癌性的（假阳性）。筛查检测不确定性的临床管理 结节经常导致不必要的、昂贵的和潜在有害的后续程序（例如，后续 CT 扫描、正电子发射断层扫描 (PET)/CT 检查、侵入性活检）。我们开发了一种令人兴奋且 新颖的基于图像的宏观血管特征来区分良性和恶性结节。我们建议 进一步开发该功能并在一系列 CT 协议和其他机构的扫描中对其进行验证。我们 还将大血管特征与临床信息（例如年龄、性别、吸烟史、 肺功能）并评估模型区分筛查检测到的良性和恶性的能力 不确定结节。我们将调查放射科医生对不确定结节的分类是否可以通过以下方法得到改善： 集成模型的输出与没有集成模型的分类的比较。此次活动的成功 该项目可能会产生一种新颖且强大的肺癌生物标志物，以准确评估屏幕检测到的肺癌生物标志物 不确定的结节，可以显着减少肺部手术期间不必要的后续手术的数量 癌症筛查。