# Macro-vasculature: A Novel Image Biomarker of Lung Cancer

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $231,482

## Abstract

ABSTRACT
Lung cancer remains the leading cause of cancer related deaths in the United States and worldwide despite
advances in early detection, treatment, and smoking cessation programs. It was reported by the National Lung
Screening Trial (NLST) that screening with low dose computed tomography (LDCT) scans may reduce lung
cancer mortality by 20% compared to chest x-ray. This conclusion ultimately led to the approval and
reimbursement for lung cancer screening using LDCT among asymptomatic adults with a history of tobacco
smoking. However, LDCT-based screening often results in a large number of indeterminate nodules that later
turn out to be non-cancerous. With increasing implementation of LDCT-based lung cancer screening in the U.S.,
the detection of indeterminate lung nodules during lung cancer screening is likely to increase. To reduce
unnecessary diagnostic procedures, such as follow-up CT scan, positron emission tomography (PET)/CT exam,
and invasive biopsies, a tool that can easily and accurately assess the malignancy and invasiveness of the
indeterminate findings will be a welcomed addition to clinical practice. Different levels of invasiveness typically
indicate different treatment plans and can often predict the treatment outcome. Compared to the investigative
efforts dedicated to discriminating benign from malignant nodules, very limited effort has been focused on
assessing the invasiveness of the suspicious nodules and explore the underlying factors associated with
invasiveness. We proposed to develop and validate a novel computer tool to non-invasively assess the
invasiveness of adenocarcinomas using LDCT scans from a large and diverse lung cancer database with
pathology outcome. We will investigate and identify how image-based features contribute to invasiveness. Our
exciting preliminary results demonstrate the feasibility of developing and implementing such a tool and its highly
translational potential. We believe that our computer tool will be a tremendously useful addition to the clinical
practice of lung cancer diagnosis and treatment. Its availability will: (1) enable a timely and accurate diagnosis
of lung cancer, (2) limit the need for further imaging, biopsies, and possible surgery, and (3) facilitate an optimal
selection of the treatment approach (e.g., surgical resection or radiotherapy). Ultimately, we want to improve
survival and the quality of life of lung cancer patients.

## Key facts

- **NIH application ID:** 10292493
- **Project number:** 3R01CA237277-02S1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Jiantao Pu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $231,482
- **Award type:** 3
- **Project period:** 2020-01-13 → 2023-01-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10292493

## Citation

> US National Institutes of Health, RePORTER application 10292493, Macro-vasculature: A Novel Image Biomarker of Lung Cancer (3R01CA237277-02S1). Retrieved via AI Analytics 2026-07-08 from https://api.ai-analytics.org/grant/nih/10292493. Licensed CC0.

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