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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

基本信息

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
项目类别：
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

项目摘要

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.

摘要