Abstract
PurposeOptimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules.Material and methodsIndependent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model.ResultsEight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939.ConclusionsOur novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.
Highlights
With approximately 160,000 deaths annually in the US, lung cancer continues to account for more cancer-related deaths than colon, prostate and breast cancer combined.[1]
Eight of the originally considered 57 quantitative radiologic features were selected by least absolute shrinkage and selection operator (LASSO) multivariate modeling
We have previously demonstrated that quantitative volumetric computed tomography (CT)-based nodule characterization effectively risk-stratifies lung nodules of the adenocarcinoma spectrum.[12,13,14,15,16]
Summary
With approximately 160,000 deaths annually in the US, lung cancer continues to account for more cancer-related deaths than colon, prostate and breast cancer combined.[1] In 2011, the National Lung Screening Trial (NLST) demonstrated a 20% relative reduction in lung cancer mortality with annual low-dose computed tomography (LDCT).[2] These encouraging results triggered the widespread endorsement of lung cancer screening. In addition to lung cancer screening the increasing utilization of diagnostic chest computed tomography (CT) results in an estimated 1.5 million incidentally discovered indeterminate lung nodules in the US annually. With the implementation of LDCT lung cancer screening for the > 10 million US adults meeting the screening eligibility criteria, this number is estimated to increase substantially.[4].
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