Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules.

ABSTRACT: Pulmonary nodules are frequently detected radiological abnormalities in lung cancer screening. Nodules of the highest- and lowest-risk for cancer are often easily diagnosed by a trained radiologist there is still a high rate of indeterminate pulmonary nodules (IPN) of unknown risk. Here, we test the hypothesis that computer extracted quantitative features ("radiomics") can provide improved risk-assessment in the diagnostic setting. Nodules were segmented in 3D and 219 quantitative features are extracted from these volumes. Using these features novel malignancy risk predictors are formed with various stratifications based on size, shape and texture feature categories. We used images and data from the National Lung Screening Trial (NLST), curated a subset of 479 participants (244 for trainin

SUBMITTER: Balagurunathan Y 

PROVIDER: S-EPMC6561979 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature