A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma.

Abstract

The purpose of the study was to compare the texture based discriminative performances between non-contrast enhanced computed tomography (NECT) and contrast-enhanced computed tomography (CECT) images in differentiating lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) patients. Eighty-seven lung cancer subjects were enrolled in the study, including pathologically proved 47 ADC patients and 40 SCC patients, and 261 texture features were extracted from the manually delineated region of interests on CECT and NECT images respectively. Fisher score was then used to select the effective discriminative texture features between groups, and the selected texture features were adopted to differentiate ADC from SCC using Support Vector Machine and Leave-one-out cross-validation. Both NECT and CECT images could achieve the same best classification accuracy of 95.4%, and most of the informative features were from the gray-level co-occurrence matrix. In addition, CECT images were found with enhanced texture features compared with NECT images, and combining texture features of CECT and NECT images together could further improve the prediction accuracy. Besides the texture feature, the tumor location information also contributed to the differential diagnosis between ADC and SCC.