Predicting EGFR mutation status in non–small cell lung cancer using artificial intelligence: a systematic review and meta-analysis

HS Nguyen, DKN Ho, NN Nguyen, HM Tran… - Academic …, 2024 - Elsevier
Rationale and Objectives Recent advancements in artificial intelligence (AI) render a
substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction …

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

X Yin, H Liao, H Yun, N Lin, S Li, Y **ang… - Seminars in cancer biology, 2022 - Elsevier
Lung cancer accounts for the main proportion of malignancy-related deaths and most
patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have …

[HTML][HTML] [18F] FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications

R Manafi-Farid, E Askari, I Shiri, C Pirich… - Seminars in nuclear …, 2022 - Elsevier
Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features

FM de Jesus, Y Yin, E Mantzorou-Kyriaki… - European Journal of …, 2022 - Springer
Background One of the challenges in the management of patients with follicular lymphoma
(FL) is the identification of individuals with histological transformation, most commonly into …

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

G Yin, Z Wang, Y Song, X Li, Y Chen, L Zhu… - Frontiers in …, 2021 - frontiersin.org
Objective The purpose of this study was to develop a deep learning-based system to
automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma …

PET/CT based EGFR mutation status classification of NSCLC using deep learning features and radiomics features

W Huang, J Wang, H Wang, Y Zhang, F Zhao… - Frontiers in …, 2022 - frontiersin.org
Purpose: This study aimed to compare the performance of radiomics and deep learning in
predicting EGFR mutation status in patients with lung cancer based on PET/CT images, and …

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

X Ge, J Gao, R Niu, Y Shi, X Shao, Y Wang… - Frontiers in …, 2023 - frontiersin.org
Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of
cancer-associated deaths. In recent years, significant progress has been achieved in basic …

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

J Gao, R Niu, Y Shi, X Shao, Z Jiang, X Ge, Y Wang… - EJNMMI research, 2023 - Springer
Background This study aims to construct radiomics models based on [18F] FDG PET/CT
using multiple machine learning methods to predict the EGFR mutation status of lung …

[HTML][HTML] Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

N Ma, W Yang, Q Wang, C Cui, Y Hu, Z Wu - Frontiers in Oncology, 2024 - frontiersin.org
Objective This study aimed to evaluate the value of 18 F-FDG PET/CT radiomics in
predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods …

Combination of 18F-fluorodeoxyglucose PET/CT radiomics and clinical features for predicting epidermal growth factor receptor mutations in lung adenocarcinoma

S Li, Y Li, M Zhao, P Wang, J **n - Korean Journal of …, 2022 - pmc.ncbi.nlm.nih.gov
Objective To identify epidermal growth factor receptor (EGFR) mutations in lung
adenocarcinoma based on 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical …