# Lung Imaging based Risk Score (LunIRIS): Decision support tool for screening CT

> **NIH VA I01** · LOUIS STOKES CLEVELAND VA MEDICAL CENTER · 2020 · —

## Abstract

ABSTRACT: Recent data from the National Lung Screening Trial (NLST) suggest that annual low-dose chest
CT scans in patients who smoke, leads to early detection of lung cancer and improves survival. CMS/Medicare
has consequently approved CT scans for lung cancer screening, and the VA National Center for Health
Promotion and Disease Prevention has adopted a similar approach. The Veteran (VA) population is at increased
risk of developing lung cancer as compared to the general population because of higher smoking rates and
increased likelihood of exposure to other carcinogens during their military service. The VA system cares for some
6.7 million mostly older male veterans each year, many of whom have long smoking histories. In a recent study,
investigators from eight VA centers across the U.S. screened more than 2,000 Veterans over two years using
criteria from the NLST. Among the 2,106 Veterans screened, a total of 1,257 (59.7%) had nodules, of which
1,184 (56.2%) required tracking. Nearly all of the positive results were negative for cancer, producing a false-
positive rate of 97.5% for human-based interpretation. In the general population, many of the lung nodules
identified by human readers as “indeterminate” or “suspicious” on chest CT trigger additional surgical
interventions (~$5K-$25K/patient) and CT exams, but >30% of these nodules on subsequent biopsies or
resection are identified as being benign. The current low false positive rate in diagnosis of nodules on screening
CT exams results in patient anxiety, and one of the reasons for poor compliance in lung cancer screening. As a
result, there is an urgent need for better image based decision support tools for improving lung cancer screening.
 PI Anant Madabhushi and his team have developed novel computerized image analysis and pattern
recognition tools for improved discrimination of cancerous from non-cancerous nodules on routine screening
chest CT scans. A significant breakthrough has been in developing a novel imaging marker called “vessel
tortuosity” for quantitatively characterizing the architectural complexity of the vasculature of a lung nodule on
chest CT scans; measurements of vessel tortuosity being significantly different between benign and malignant
lung nodules. Additionally our group has also identified other highly predictive image features that aim to
capture (1) subtle textural patterns of the microarchitecture within and immediately outside the nodule, and (2)
subtle 3D shape patterns of the nodule. Each of these imaging markers has been independently shown to
have an area under the receiver operating characteristic curve (AUC) ranging from 77-87% in distinguishing
malignant from benign nodules in a validation set of N=145 patients. By contrast, on this cohort an expert chest
radiologist and pulmonologist had a maximum AUCs of 69-72%. More interestingly, on this cohort combining
machine based interpretations with human readers resulted in an improvement of 30% in the AUC va...

## Key facts

- **NIH application ID:** 9850850
- **Project number:** 5I01BX004121-02
- **Recipient organization:** LOUIS STOKES CLEVELAND VA MEDICAL CENTER
- **Principal Investigator:** Anant Madabhushi
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850850

## Citation

> US National Institutes of Health, RePORTER application 9850850, Lung Imaging based Risk Score (LunIRIS): Decision support tool for screening CT (5I01BX004121-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9850850. Licensed CC0.

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