Oncotarget

Research Papers:

Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images

Jung Min Bae, Ji Yun Jeong, Ho Yun Lee, Insuk Sohn, Hye Seung Kim, Ji Ye Son, O Jung Kwon, Joon Young Choi, Kyung Soo Lee and Young Mog Shim _

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Oncotarget. 2017; 8:523-535. https://doi.org/10.18632/oncotarget.13476

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Abstract

Jung Min Bae1,*, Ji Yun Jeong2,*, Ho Yun Lee1, Insuk Sohn3, Hye Seung Kim3, Ji Ye Son1, O Jung Kwon4, Joon Young Choi5, Kyung Soo Lee1, Young Mog Shim6

1Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

2Department of Pathology, Kyungpook National University Medical Center, Kyungpook National University School of Medicine, Daegu 702-210, Korea

3Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

4Division of Respiratory and Critical Medicine of the Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

5Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 135-710, Korea

6Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul 135-710, Korea

*These authors contributed equally to this work

Correspondence to:

Ho Yun Lee, email: [email protected]

Young Mog Shim, email: [email protected]

Keywords: lung adenocarcinoma, heterogeneity, radiomics, texture analysis, dual energy CT

Received: June 29, 2016     Accepted: November 14, 2016     Published: November 21, 2016

ABSTRACT

Purpose: To evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment.

Results: Pathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514–1), 0.8610 (95% CI: 0.7547–0.9672), and 0.8394 (95% CI: 0.7045–0.9743), respectively.

Materials and Methods: A total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades.

Conclusions: Quantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.


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