Relation of EGFR Mutation Status to Metabolic Activity in Localized Lung Adenocarcinoma and Its Influence on the Use of FDG PET/CT Parameters in Prognosis

2018 ◽  
Vol 210 (6) ◽  
pp. 1346-1351 ◽  
Author(s):  
Yong-il Kim ◽  
Jin Chul Paeng ◽  
Young Sik Park ◽  
Gi Jeong Cheon ◽  
Dong Soo Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Metabolic Activity ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Mutation Status ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Guotao Yin ◽  
Ziyang Wang ◽  
Yingchao Song ◽  
Xiaofeng Li ◽  
Yiwen Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Adenocarcinoma ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Learning Models ◽  
Data Set ◽  
Mutation Status ◽  
Pet Ct ◽  
Testing Data ◽  
Fdg Pet Ct

ObjectiveThe purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).MethodsThree hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET.ResultsThe AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SECT (p<0.05).ConclusionThe stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.

scholarly journals Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Guotao Yin ◽  
Ziyang Wang ◽  
Yingchao Song ◽  
Xiaofeng Li ◽  
Yiwen Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Adenocarcinoma ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Ct Imaging ◽  
Mutation Status ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Min Zhang ◽  
Yiming Bao ◽  
Weiwei Rui ◽  
Chengfang Shangguan ◽  
Jiajun Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features

2020 ◽  
Vol 9 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Qiufang Liu ◽  
Dazhen Sun ◽  
Nan Li ◽  
Jinman Kim ◽  
Dagan Feng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

2019 ◽  
Vol 47 (5) ◽  
pp. 1137-1146 ◽  
Author(s):  
Jianyuan Zhang ◽  
Xinming Zhao ◽  
Yan Zhao ◽  
Jingmian Zhang ◽  
Zhaoqi Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Fdg Pet ◽  
Egfr Mutation ◽  
Small Cell ◽  
Small Cell Lung ◽  
Mutation Status ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Metabolic activity of visceral fat and immune-related organs evaluated by 18F-FDG PET/CT is associated with the metabolic syndrome

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Pahk ◽  
H.W Kwon ◽  
J.S Eo ◽  
H.S Seo ◽  
S Kim
Keyword(s):  
Metabolic Syndrome ◽  
Systemic Inflammation ◽  
Metabolic Activity ◽  
Fdg Pet ◽  
Inflammatory Activity ◽  
The Metabolic Syndrome ◽  
Inflammatory Status ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Abstract Background The risk of cardiovascular disease (CVD) is elevated in metabolic syndrome (MS) and is related to the inflammatory activity of visceral adipose tissue (VAT). We investigated whether the metabolic activity in VAT, assessed by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT), is associated with systemic inflammatory status, and related to the number of MS components. Methods 18F-FDG PET/CT was performed in a total of 203 subjects: 59 without an MS component; M(0), 92 with one or two MS components; M(1–2), and 52 with MS. Metabolic activity of VAT was evaluated using the mean standardized uptake value (SUVmean) and the maximum SUV (SUVmax). Metabolic activities of immune-related organs such as spleen and bone marrow (BM) were evaluated using the SUVmax. Results VAT SUVmax correlated with high-sensitivity C-reactive protein (hsCRP) and the SUVmax of spleen and BM, which reflect the status of systemic inflammation. Both hsCRP and the SUVmax of the spleen and BM were higher in the MS group than in the M(1–2) or M(0) groups. In VAT, SUVmax increased with increasing number of MS components, while SUVmean decreased. Conclusions The SUVmax of VAT assessed by 18F-FDG PET/CT could reflect the inflammatory activity of VAT which is increased in the MS patients with systemic inflammation. Funding Acknowledgement Type of funding source: None

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