Research Papers:
Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images
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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.