# Project 4: Risk stratification for pulmonary nodules detected by CT imaging using plasma and imaging biomarkers

> **NIH NIH P50** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $594,498

## Abstract

Project Summary/Abstract – Project 4
Lung cancer is the leading cause of cancer deaths worldwide with >159,000 deaths annually in the US alone.
The National Lung Screening Trial (NLST) employed low-dose Computed Tomography (CT) imaging of the
chest to screen for lung cancer in a high-risk population (smokers aged 55-74). This study demonstrated a
20% reduction in mortality in the group receiving CTs when compared to standard care and has led to
generalized acceptance of lung cancer screening in heavy smokers. Unfortunately, pulmonary nodules are a
relatively common finding with 25-56% of smokers >50 years of age having CT identifiable pulmonary nodules
but less than 2.5% of these actually were cancerous. For diagnosis of incidentally detected pulmonary nodules,
current guidelines call for additional imaging and/or invasive biopsy procedures. For both of these scenarios
we propose to combine two novel approaches to improve risk stratification for subjects with pulmonary
modules. The first involves an antibody array platform for proteomic, glycomic, and autoantibody-antigen
complex interrogation that has yielded a four-marker panel with an area under the ROC curve (AUC) of 0.82 in
prediagnostic samples and 0.83 in a validation diagnostic set of malignant and benign nodules. The second
novel component is the analysis of quantitative nodule features extracted from CT images using the methods
of 'radiomics'. We have developed a validated radiomics pipeline that used machine learning algorithms for
image texture features that when combined with radiologist-described shape, or semantic features yielded an
AUC of 0.82 using the same diagnostic sample set described above. We have created a rule that combines
clinical factors (age, smoking etc.), plasma biomarkers, radiomic CT image semantic and texture features for
classification of CT-detected nodules as malignant or benign. The addition of both radiomic and biomarkers to
the rule significantly increase the AUC (p<0.005) over clinical and semantic CT measures alone. This rule will
be tested first in a Vanderbilt CVC incidental/diagnostic cohort, then fixed and tested in the Detection of Early
lung Cancer Among Military Personnel Study 1 (DECAMP-1) cohort (Aim 1) with the goal of improving nodule
evaluation. We will also test the rule in the NLST screening cohort (Aim 2) to create a final rule that models
lung cancer early detection. In Aim 3 we will test the fixed rules from aims 1 and 2 in University of Colorado
diagnostic and DECAMP-2 (prediagnostic) cohorts, respectively.

## Key facts

- **NIH application ID:** 9986730
- **Project number:** 5P50CA228944-02
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** PAUL D. LAMPE
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $594,498
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986730

## Citation

> US National Institutes of Health, RePORTER application 9986730, Project 4: Risk stratification for pulmonary nodules detected by CT imaging using plasma and imaging biomarkers (5P50CA228944-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9986730. Licensed CC0.

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