Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma

// E-Ryung Choi 1 , Ho Yun Lee 1 , Ji Yun Jeong 2 , Yoon-La Choi 3 , Jhingook Kim 4 , Jungmin Bae 1 , Kyung Soo Lee 1 , Young Mog Shim 4 1 Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 2 Department of Pathology, Kyungpook National University Hospital, Kyungpook National University School of Medicine, Daegu, Korea 3 Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 4 Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea Correspondence to: Ho Yun Lee, email: hoyunlee96@gmail.com Keywords: lung adenocarcinoma, heterogeneity, radiomics, quantitative image variables, dual energy CT Received: May 12, 2016      Accepted: July 19, 2016      Published: August 30, 2016 ABSTRACT We aimed to compare quantitative radiomic parameters from dual-energy computed tomography (DECT) of lung adenocarcinoma and pathologic complexity. A total 89 tumors with clinical stage I/II lung adenocarcinoma were prospectively included. Fifty one radiomic features were assessed both from iodine images and non-contrast images of DECT datasets. Comprehensive histologic subtyping was evaluated with all surgically resected tumors. The degree of pathologic heterogeneity was assessed using pathologic index and the number of mixture histologic subtypes in a tumor. Radiomic parameters were correlated with pathologic index. Tumors were classified as three groups according to the number of mixture histologic subtypes and radiomic parameters were compared between the three groups. Tumor density and 50 th through 97.5 th percentile Hounsfield units (HU) of histogram on non-contrast images showed strong correlation with the pathologic heterogeneity. Radiomic parameters including 75 th and 97.5 th percentile HU of histogram, entropy, and inertia on 1-, 2- and 3 voxel distance on non-contrast images showed incremental changes while homogeneity showed detrimental change according to the number of mixture histologic subtypes (all Ps < 0.05). Radiomic variables from DECT of lung adenocarcinoma reflect pathologic intratumoral heterogeneity, which may help in the prediction of intratumoral heterogeneity of the whole tumor.