Indeterminate pulmonary nodules (IPNs) are a common finding on CT imaging of the lungs and often require costly and invasive testing to diagnose. IPN diagnosis is especially difficult in regions like Middle Tennessee where fungal diseases such as histoplasmosis are endemic.
Hannah Marmor, MD, MPH, and colleagues combined imaging and blood biomarkers with the Mayo Clinic prediction model to improve IPN diagnostic accuracy. In a study of 281 patients from three geographically diverse cohorts, they measured serum levels of a cancer tumor marker and histoplasmosis antibodies, and they performed quantitative imaging analysis.
The researchers found that adding cancer, fungal and imaging biomarkers to the Mayo Clinic model improved diagnostic accuracy for IPNs. The combination model correctly reclassified clinically difficult, intermediate risk IPNs into low- or high-risk categories.
The findings, reported in The Journal of Thoracic and Cardiovascular Surgery, suggest that adding biomarkers to the diagnostic algorithm for IPNs might decrease invasive testing of benign nodules and reduce time to diagnosis for cancer.
Stephen Deppen, PhD, is the corresponding author of the report. Other VUMC authors include Michael N. Kammer, PhD, Maren Shipe, MD, MPH, Valerie Welty, Khushbu Patel, MD, Caroline Godfrey, MD, Fabien Maldonado, MD, Heidi Chen, PhD, and Eric Grogan, MD, MPH. They were joined by collaborators at Vanderbilt University, Boston Medical Center and University of Pittsburgh Medical Center.
The research was supported by the National Institutes of Health (grants CA152662 and CA106183).
