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.

Public Health Relevance

We propose to investigate the role of the macro-vasculature surrounding indeterminate lung nodules depicted on CT images to discriminate screen-detected benign and malignant nodules. The goal is to improve lung cancer screening by reducing the number of unnecessary procedure associate the high rate of false positive detections.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA237277-02
Application #
10084850
Study Section
Clinical Translational Imaging Science Study Section (CTIS)
Program Officer
Liu, Christina
Project Start
2020-01-13
Project End
2025-01-01
Budget Start
2021-01-02
Budget End
2022-01-01
Support Year
2
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213