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Medicine (Baltimore). 2015 Oct;94(41):e1753. doi: 10.1097/MD.0000000000001753.

Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach.

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From the Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (HJY, HYL, J-HK, KSL); Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Seoul, Korea (IS, HK); Department of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (JHC, JK); Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (Y-LC); Department of Nursing, Lung and Esophageal Cancer Center, Samsung Comprehensive Cancer Center, Samsumg Medical Center, Seoul, Korea (GL); and Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Korea (HJY).


Quantitative imaging using radiomics can capture distinct phenotypic differences between tumors and may have predictive power for certain phenotypes according to specific genetic mutations. We aimed to identify the clinicoradiologic predictors of tumors with ALK (anaplastic lymphoma kinase), ROS1 (c-ros oncogene 1), or RET (rearranged during transfection) fusions in patients with lung adenocarcinoma.A total of 539 pathologically confirmed lung adenocarcinomas were included in this retrospective study. The baseline clinicopathologic characteristics were retrieved from the patients' medical records and the ALK/ROS1/RET fusion status was reviewed. Quantitative computed tomography (CT) and positron emission tomography imaging characteristics were evaluated using a radiomics approach. Significant features for the fusion-positive tumor prediction model were extracted from all of the clinicoradiologic features, and were used to calculate diagnostic performance for predicting 3 fusions' positivity. The clinicoradiologic features were compared between ALK versus ROS1/RET fusion-positive tumors to identify the clinicoradiologic similarity between the 2 groups.The fusion-positive tumor prediction model was a combination of younger age, advanced tumor stage, solid tumor on CT, higher values for SUV(max) and tumor mass, lower values for kurtosis and inverse variance on 3-voxel distance than those of fusion-negative tumors (sensitivity and specificity, 0.73 and 0.70, respectively). ALK fusion-positive tumors were significantly different in tumor stage, central location, SUV(max), homogeneity on 1-, 2-, and 3-voxel distances, and sum mean on 2-voxel distance compared with ROS1/RET fusion-positive tumors.ALK/ROS1/RET fusion-positive lung adenocarcinomas possess certain clinical and imaging features that enable good discrimination of fusion-positive from fusion-negative lung adenocarcinomas.

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