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• W2914397128 abstract "•Quantitative DECT metrics were significantly different between SCLC and NSCLC. •CT morphologic features were useful in differentiating SCLC from NSCLC. •Differentiation performance was improved after adding DECT metrics to CT features. Aim To evaluate the clinical usefulness of quantitative dual-energy (DE) computed tomography (CT) iodine enhancement metrics combined with morphological CT features in distinguishing small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC). Materials and methods One hundred and six untreated lung cancer patients who underwent DECT before biopsy or surgery were prospectively enrolled. Twenty-seven routine CT descriptors, including tumour location, size, shape, margin, enhancement heterogeneity, and internal and surrounding structures, and associated findings were assessed and DECT parameters were measured in all patients. Multiple logistic regression analyses were applied to identify independent predictors of SCLC. The area under the receiver operating characteristic curve was compared between CT features combined with DECT metrics and CT features alone for distinguishing SCLC from NSCLC. Results Histology revealed NSCLC in 80 and SCLC in 26 patients. In univariate analysis, 12 morphological CT features and two DECT metrics differed significantly between NSCLC and SCLC. When DECT parameters were combined with CT features for multivariate analysis, the independent predictors of SCLC were large tumour size, central location, confluent mediastinal lymphadenopathy, homogeneous enhancement, absence of coarse spiculation, and lower iodine density and iodine ratio (all p<0.05). The area under the receiver operating characteristic curve was improved from 0.908 to 0.981 after adding DECT metrics compared with CT features alone (p=0.007). Conclusion The combination of DECT measures and CT morphological features can be used to distinguish SCLC from NSCLC, with higher diagnostic performance compared with CT morphological features alone. To evaluate the clinical usefulness of quantitative dual-energy (DE) computed tomography (CT) iodine enhancement metrics combined with morphological CT features in distinguishing small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC). One hundred and six untreated lung cancer patients who underwent DECT before biopsy or surgery were prospectively enrolled. Twenty-seven routine CT descriptors, including tumour location, size, shape, margin, enhancement heterogeneity, and internal and surrounding structures, and associated findings were assessed and DECT parameters were measured in all patients. Multiple logistic regression analyses were applied to identify independent predictors of SCLC. The area under the receiver operating characteristic curve was compared between CT features combined with DECT metrics and CT features alone for distinguishing SCLC from NSCLC. Histology revealed NSCLC in 80 and SCLC in 26 patients. In univariate analysis, 12 morphological CT features and two DECT metrics differed significantly between NSCLC and SCLC. When DECT parameters were combined with CT features for multivariate analysis, the independent predictors of SCLC were large tumour size, central location, confluent mediastinal lymphadenopathy, homogeneous enhancement, absence of coarse spiculation, and lower iodine density and iodine ratio (all p<0.05). The area under the receiver operating characteristic curve was improved from 0.908 to 0.981 after adding DECT metrics compared with CT features alone (p=0.007). The combination of DECT measures and CT morphological features can be used to distinguish SCLC from NSCLC, with higher diagnostic performance compared with CT morphological features alone." @default.
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• W2914397128 date "2019-04-01" @default.
• W2914397128 modified "2023-10-18" @default.
• W2914397128 title "Clinical utility of quantitative dual-energy CT iodine maps and CT morphological features in distinguishing small-cell from non-small-cell lung cancer" @default.
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• W2914397128 doi "https://doi.org/10.1016/j.crad.2018.10.012" @default.
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