Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

ABSTRACT:

Background

Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.

Methods

Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.

Results

Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer

SUBMITTER: Marmor HN 

PROVIDER: S-EPMC10057862 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature