2019
DOI: 10.1186/s40644-019-0221-9
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CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma

Abstract: Objective To identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma. Materials and methods This retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was … Show more

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Cited by 26 publications
(26 citation statements)
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“…The ROIs were required to include the area of necrosis and bleeding within the tumor and excluded perienteric fat and intestinal contents. To correct for acquisition-related differences of differing voxel resolutions in the two different CT scanners, voxel dimensions (mm) of each iodine-based MD image dataset were isotropically resampled to a common voxel spacing 0.5 × 0.5 × 0.5 mm 3 ( x, y, z ) via linear interpolation algorithm (18, 19). Next, a total of 606 radiomics features for each CRC patient were extracted via Artificial Intelligent Kit (GE Healthcare) in concordance with the reference manual by the “Image Biomarker Standardization Initiative.” These features were divided into four groups: (1) first-order histogram features ( n = 42); (2) second-order texture features: gray level co-occurrence matrix (GLCM) ( n = 240), Haralick features ( n = 10); (3) grey-level zone size matrix (GLZSM) ( n = 11); and (4) Gaussian transform ( n = 303).…”
Section: Methodsmentioning
confidence: 99%
“…The ROIs were required to include the area of necrosis and bleeding within the tumor and excluded perienteric fat and intestinal contents. To correct for acquisition-related differences of differing voxel resolutions in the two different CT scanners, voxel dimensions (mm) of each iodine-based MD image dataset were isotropically resampled to a common voxel spacing 0.5 × 0.5 × 0.5 mm 3 ( x, y, z ) via linear interpolation algorithm (18, 19). Next, a total of 606 radiomics features for each CRC patient were extracted via Artificial Intelligent Kit (GE Healthcare) in concordance with the reference manual by the “Image Biomarker Standardization Initiative.” These features were divided into four groups: (1) first-order histogram features ( n = 42); (2) second-order texture features: gray level co-occurrence matrix (GLCM) ( n = 240), Haralick features ( n = 10); (3) grey-level zone size matrix (GLZSM) ( n = 11); and (4) Gaussian transform ( n = 303).…”
Section: Methodsmentioning
confidence: 99%
“…All scanning was performed on a Gemini GXL 16-slice PET/CT system (Philips) with 18 Fuorodeoxyglucose (FDG) (radiochemical identity/purity > 95%) provided by Andico. The patient had fasted for more than 6 hours.…”
Section: Image Acquisition and Analysismentioning
confidence: 99%
“…The patient's height and weight and level of fasting blood glucose (<6.1 mmol/L) were measured. In the resting state, 222-492.1 MBq (6-13.3 mCi) 18 F-FDG was injected via the dorsal vein of the hand. PET/CT was performed 50-60 minutes after injection, during which the patient was resting in a dark room.…”
Section: Image Acquisition and Analysismentioning
confidence: 99%
“…A large number of studies have shown that the radiomic analysis can be used to predict the mutation status of several oncogenes (16,17). Currently, most studies in lung cancer have been done in primary tumors using computed tomography (CT) images (18)(19)(20)(21)(22). For example, Gevaert et al used CT images-based signature of primary lung tumors to predict EGFR mutation status (23).…”
Section: Introductionmentioning
confidence: 99%