Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing.
Hannah N Marmor, Stephen A Deppen, Valerie Welty, Michael N Kammer, Caroline M Godfrey, Khushbu Patel, Fabien Maldonado, Heidi Chen, Sandra L Starnes, David O Wilson, Ehab Billatos, Eric L Grogan
Author Information
Hannah N Marmor: Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Stephen A Deppen: Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Valerie Welty: Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Michael N Kammer: Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Caroline M Godfrey: Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Khushbu Patel: Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Fabien Maldonado: Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Heidi Chen: Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
Sandra L Starnes: Division of Thoracic Surgery, University of Cincinnati, Cincinnati, Ohio. ORCID
David O Wilson: Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. ORCID
Ehab Billatos: Section of Pulmonary and Critical Care Medicine, Boston Medical Center, Boston, Massachusetts. ORCID
Eric L Grogan: Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. ORCID
BACKGROUND: Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS: Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for Histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM Histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS: We included 327 patients; 157 from Histoplasmosis-endemic regions. The combined biomarker model including radiomics, Histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic Histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS: fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of Histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT: Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.
