# Novel Integrative Approach for the Early Detection of Lung Cancer using Repeated Measures

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2021 · $682,056

## Abstract

PROJECT SUMMARY
Early detection of lung cancer among asymptomatic individuals is a priority for reducing mortality of the number
one cancer killer worldwide. Most lung cancers are first detected as indeterminate pulmonary nodules (IPNs).
While the vast majority of IPNs are benign, those malignant ones present with specific features that should allow
for the early discrimination and intervention. We have recently completed a study demonstrating the value of
structural imaging features analysis in providing improved accuracy in detection of cancers among IPNs with
accuracy of over 90% trained in the NLST and validated in two independent cohorts. The AUC increased from
baseline risk estimate of disease using clinical parameters (Mayo model) 0.78 to 0.84 and from 0.82 to 0.92 in
two independent validation cohorts. Similarly, we tested the added value of our high sensitivity hsCYFRA 21-1
assay in three populations of lung nodules and obtained similar added value to the MAYO model. Finally, we
identified signatures predictive of lung cancer using large scale data mining in the electronic health record (EHR).
The performance of the performance of the established imaging predictor, hsCYFRA concentrations and EHR
trajectories will be validated in a prospective cohort. In an innovative partnership between pulmonary oncology,
radiology, machine learning, and data science experts at Vanderbilt, we propose to integrate the layer of clinical
information accessible in the EHR to improve the noninvasive diagnosis accuracy. In addition, we propose to
take advantage of repeated measures to improve the accuracy of the prediction of cancer and to reduce the time
to diagnosis. We therefore propose the following aims. In Aim 1 we will validate advanced quantitative imaging
analyses to distinguish early benign from malignant IPNs based on repeated measures of 1000 individuals. In
Aim 2. We will test in 150 individuals with lung nodules the added value of repeated measures of hsCYFRA 21-
1 protein blood biomarker in diagnostic accuracy over the baseline concentrations of the biomarker. In Aim 3 we
will test a deep learning strategy from the EHR of 20,000 patients from VUMC to identify patterns likely to improve
the early detection of lung cancer, and in Aim 4 we will test the added value of monitoring changes in levels of
the markers for early detection using repeated pre-diagnosis chest CT studies, serum analysis of hsCYFRA 21-
1, and EHR patterns from our lung cancer screening program. Built upon strong preliminary data and unique
resources from VUMC that include access to large imaging and HER data sources this novel integrative study
has the potential to generate highly impactful and translatable results to reduce false positive rates among IPNs,
and morbidity and mortality from lung cancer. This application responds to PAR 19-264 using low-dose lung
screening computed tomography longitudinal analysis integrated with a lead serum biomarker and the power of
artificia...

## Key facts

- **NIH application ID:** 10069919
- **Project number:** 1R01CA253923-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Bennett A. Landman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $682,056
- **Award type:** 1
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10069919

## Citation

> US National Institutes of Health, RePORTER application 10069919, Novel Integrative Approach for the Early Detection of Lung Cancer using Repeated Measures (1R01CA253923-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10069919. Licensed CC0.

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