Original Article
Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules
Abstract
Background: Texture analysis is a computer tool that enables quantification of gray-level patterns, pixel interrelationships, and spectral properties of an image. It can enhance visual methods of image analysis. Primary lung cancer and granulomatous nodules have identical CT imaging features. The purpose of this study was to assess the sensitivity and specificity of CT texture analysis in differentiating lung cancer and granulomas.
Methods: This retrospective study evaluated 55 patients with primary lung cancer and granulomatous nodules who had contrast-enhanced (CE) and/or non-contrast-enhanced (NCE) CT within 3 months of biopsy. Textural features were extracted from 61 nodules. Mann-Whitney U tests were used to compare values for nodules. Receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with histopathology as outcome. Combinations of features were entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.
Results: The model generated by sum of squares, sum difference, and sum entropy features for NCE CT yielded 88% sensitivity and 92% specificity (AUC =0.90±0.06, P<0.0001). For nodules with fluorodeoxyglucose positron emission tomography (FDG-PET)/CT, sensitivity for detection of lung cancer was 79.2% (CI: 57.8–92.9%), specificity was 38.5% (CI: 13.9–68.4%) and accuracy was 64.8%.
Conclusions: Quantitative CT texture analysis has the potential to differentiate primary lung cancer and granulomatous lesions.
Methods: This retrospective study evaluated 55 patients with primary lung cancer and granulomatous nodules who had contrast-enhanced (CE) and/or non-contrast-enhanced (NCE) CT within 3 months of biopsy. Textural features were extracted from 61 nodules. Mann-Whitney U tests were used to compare values for nodules. Receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with histopathology as outcome. Combinations of features were entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity.
Results: The model generated by sum of squares, sum difference, and sum entropy features for NCE CT yielded 88% sensitivity and 92% specificity (AUC =0.90±0.06, P<0.0001). For nodules with fluorodeoxyglucose positron emission tomography (FDG-PET)/CT, sensitivity for detection of lung cancer was 79.2% (CI: 57.8–92.9%), specificity was 38.5% (CI: 13.9–68.4%) and accuracy was 64.8%.
Conclusions: Quantitative CT texture analysis has the potential to differentiate primary lung cancer and granulomatous lesions.
