A diagnostic model for malignant pulmonary nodules

NIH RePORTER · NIH · R21 · $168,019 · view on reporter.nih.gov ↗

Abstract

The early detection of lung cancer by low-dose computed tomography (LDCT) followed by effective treatments, including immunotherapy, can reduce the mortality. LDCT is now recommended for lung cancer screening in smokers. However, more than 25% of smokers screened by LDCT have indeterminate pulmonary nodules (PNs), of which only 4% are finally diagnosed to be lung cancers, whereas more than 95% are benign diseases, resulting in over-diagnosis. As a result, large numbers of smokers with indeterminate PNs are referred for invasive biopsies and expensive 2-year multiple follow-up examinations, which carry their own morbidity and mortality. Therefore, there is an unmet clinical need for accurately distinguishing malignant PN (lung cancer) from benign PN in smokers with LDCT-found PNs. However, none of biomarkers and radiological features of PNs provides sufficient diagnostic values required in the clinics for accurately identifying malignant PNs. The objective of this proposed project is to develop a test for specifically differentiating malignant from benign PN. The target population of this test will be smokers with LDCT-found PNs. Its future use in the clinics will spare smokers with benign PNs from invasive biopsies and expensive multiple follow-up examinations, while facilitating effective treatments to be instantly initiated for lung cancer. Therefore, the test could complement LDCT for the early detection lung cancer, and thereby reduce the mortality and cost.

Key facts

NIH application ID
9970448
Project number
5R21CA240556-02
Recipient
UNIVERSITY OF MARYLAND BALTIMORE
Principal Investigator
Feng Jiang
Activity code
R21
Funding institute
NIH
Fiscal year
2020
Award amount
$168,019
Award type
5
Project period
2019-07-02 → 2021-06-30