Oncotarget

Research Papers:

Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma

E-Ryung Choi, Ho Yun Lee _, Ji Yun Jeong, Yoon-La Choi, Jhingook Kim, Jungmin Bae, Kyung Soo Lee and Young Mog Shim

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Oncotarget. 2016; 7:67302-67313. https://doi.org/10.18632/oncotarget.11693

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Abstract

E-Ryung Choi1, Ho Yun Lee1, Ji Yun Jeong2, Yoon-La Choi3, Jhingook Kim4, Jungmin Bae1, Kyung Soo Lee1, Young Mog Shim4

1Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

2Department of Pathology, Kyungpook National University Hospital, Kyungpook National University School of Medicine, Daegu, Korea

3Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

4Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

Correspondence to:

Ho Yun Lee, email: [email protected]

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 50th through 97.5th percentile Hounsfield units (HU) of histogram on non-contrast images showed strong correlation with the pathologic heterogeneity. Radiomic parameters including 75th and 97.5th 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.


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