Journal
FRONTIERS IN ONCOLOGY
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2019.01062
Keywords
epidermal growth factor receptor mutation; F-18-FDG PET/CT imaging; non-small cell lung cancer; prediction; radiomics
Categories
Funding
- National Natural Science Foundation of China [81601377, 81501984, 2018ZX09201015]
- Tianjin Natural Science Fund [16JCZDJC35200, 17JCYBJC25100, 18PTZWHZ00100, H2018206600]
- Science & Technology Development Fund of Tianjin Education Commission for Higher Education [2018KJ057, 2018KJ061]
- Tianjin Medical University Cancer Institute and Hospital Fund [Y1601, B1605, B1719]
- Incubation Project of the National Clinical Research Center for Cancer [N14B09]
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Radiomics has become an area of interest for tumor characterization in F-18-Fluorodeoxyglucose positron emission tomography/computed tomography (F-18-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.
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