Comparative analysis of computed tomography texture features between pulmonary inflammatory nodules and lung cancer

Abstract

Objective: To investigate the value of computed tomography (CT) texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Methods: The image data of 54 patients with lung cancer and 36 patients with pulmonary inflammatory nodules were retrospectively collected in our hospital. All the patients received chest CT scan. CT texture analysis of entropy, correlation degree and contrast ratio were performed by the MaZda software. The receiver operating characteristic curve (ROC) was established and the area under the curve (AUC) was calculated to evaluate the value of CT texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Results: In the lung cancer group, the value of entropy, correlation degree and contrast ratio were 1.58±0.07, 0.02±0.17 and 8.79±2.59, respectively. In the inflammatory nodules group, the value of entropy, correlation degree and contrast ratio were 1.51±0.04, 0.22±0.16 and 12.53±2.24, respectively. The differences were all statistically significant (P values were 0.008, 0.027, and 0.006, respectively) between two groups. There was not statistically significant difference (P>0.05) in the CT values between the lung cancer group and the inflammatory nodule group based on the non-contrast enhanced CT scan. Meanwhile, there was no statistically significant difference (P>0.05) in the value of entropy, correlation degree or contrast ratio between two groups based on arterial phase or venous phase of contrast enhanced CT. The ROC analysis showed that the AUC in differentiating the lung cancer and inflammatory nodules was 0.821, 0.778 and 0.875, respectively. The AUC of combination of three phases was 0.931, which was higher than the AUC of entropy, correlation degree and contrast ratio respectively (P<0.01). The sensitivity was 88.9%, and the specificity was 87.5%. Conclusion: CT texture analysis is a high-potential image analysis method, which can provide more information for the differential diagnosis of benign and malignant pulmonary nodules.