Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules.

Autor:	Uthoff JM; Department of Radiology, University of Iowa, Iowa City, Iowa, United States.; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States.; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, Iowa, United States., Mott SL; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, Iowa, United States., Larson J; Department of Radiology, University of Iowa, Iowa City, Iowa, United States., Neslund-Dudas CM; Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, United States.; Henry Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan, United States., Schwartz AG; Karmanos Cancer Institute, Wayne State University, Detroit, Michigan, United States., Sieren JC; Department of Radiology, University of Iowa, Iowa City, Iowa, United States.; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States.; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, Iowa, United States.
Jazyk:	angličtina
Zdroj:	Chronic obstructive pulmonary diseases (Miami, Fla.) [Chronic Obstr Pulm Dis] 2022 Apr 29; Vol. 9 (2), pp. 154-164.
DOI:	10.15326/jcopdf.2021.0271
Abstrakt:	Background: Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment. Methods: A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign). Results: The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79). Conclusions: Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis. (JCOPDF © 2021.)
Databáze:	MEDLINE
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