# Lung cancer screening efficacy enhanced through radiomic and epigenetic biomarkers

> **NIH NIH R01** · UNIVERSITY OF IOWA · 2022 · $353,419

## Abstract

ABSTRACT
 Smoking is the largest risk factor for both lung cancer and obstructive lung disease. The National Lung
Screening Trial (NLST) enrolled subjects who reported a cigarette smoking history of at least 30 pack years and
showed that annual low-dose computed tomography (LDCT) screening could reduce mortality from lung cancer
by approximately 16%, compared to conventional chest x-ray. However, it remains clinically challenging to
efficiently distinguish the small number of malignant nodules from the many benign lung nodules detected with
screening. In addition, the chest LDCT data captured during screening also has untapped utility in quantitatively
evaluating obstructive lung disease.
 LDCT captures a wealth of information that can be automatically and objectively quantified and extracted
from the image data using computer algorithms. We have methods for automated segmentation of structures of
interest from the image data and will extract hundreds of radiological biomarkers focused on pulmonary nodules,
peri-nodular lung parenchyma, the whole lung, and capture lobar heterogeneity. This study will also incorporate
an objective epigenetic biomarker of smoking history via measurement of DNA methylation at cg05575921. Our
epigenetic biomarker has been shown to strongly predict smoking intensity by several studies. We will use the
objective radiological and epigenetic biomarkers and machine learning approaches to predict both (1) the risk of
lung cancer and (2) rapid obstructive lung disease progression in the NLST screening population. We
hypothesize that incorporating DNA methylation at cg05575921 will be a valuable addition to both prediction
models. Determining the outcome of the hypothesis will guide if this epigenetic biomarker should be incorporated
in prospective lung cancer screening studies.
 This project will have impact as it will result in an improved automatic risk prediction algorithm to guide
management in subjects with a lung nodule detected by LDCT screening. This approach can facilitate rapid
treatment for those with cancer and prevent complications from invasive diagnostic testing as well as
unnecessary radiation exposure from diagnostic imaging in those with benign lesions. Predicting rapid
obstructive lung disease progression may be beneficial for clinician/subject shared decision-making discussions
and targeted smoking cessation interventions in addition to improving lung cancer prediction.

## Key facts

- **NIH application ID:** 10518050
- **Project number:** 1R01CA267820-01A1
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** Jessica C Sieren
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $353,419
- **Award type:** 1
- **Project period:** 2022-07-11 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10518050

## Citation

> US National Institutes of Health, RePORTER application 10518050, Lung cancer screening efficacy enhanced through radiomic and epigenetic biomarkers (1R01CA267820-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10518050. Licensed CC0.

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