Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma

Radiomics Signature as a Predictive Factor for EGFR Mutations in Advanced Lung Adenocarcinoma

Purpose: To develop and validate a radiomic signature to identify EGFR mutations in patients with advanced lung adenocarcinoma.Methods: This study involved 201 patients with advanced lung adenocarcinoma (140 in the training cohort and 61 in the validation cohort). A total of 396 features were extrac...

Full description

Bibliographic Details
Main Authors:	Duo Hong, Ke Xu, Lina Zhang, Xiaoting Wan, Yan Guo
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2020-01-01
Series:	Frontiers in Oncology
Subjects:	advanced lung adenocarcinoma tomography epidermal growth factor receptor mutation radiomics
Online Access:	https://www.frontiersin.org/article/10.3389/fonc.2020.00028/full

Similar Items

CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses
by: Dongdong Mei, et al.
Published: (2018-12-01)

Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB–IV) Lung Adenocarcinoma Patients
by: Duo Hong, et al.
Published: (2021-05-01)

Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma
by: Yang B, et al.
Published: (2019-08-01)

The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma
by: Qin Liu, et al.
Published: (2020-06-01)

Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations
by: Bin Yang, et al.
Published: (2020-11-01)

Detection and Analysis of EGFR and KRAS Mutation with Lung Adenocarcinoma
by: Hui ZHANG, et al.
Published: (2015-11-01)

Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas
by: Jiyoung Yoon, et al.
Published: (2020-04-01)

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
by: Fangyi Xu, et al.
Published: (2020-08-01)

Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma
by: Fangyi Xu, et al.
Published: (2020-10-01)

Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients
by: Vincenza Granata, et al.
Published: (2021-08-01)

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics
by: Hyun Jung Yoon, et al.
Published: (2020-09-01)

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma
by: Wei Zhao, et al.
Published: (2020-01-01)

Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma
by: Yunyu Xu, et al.
Published: (2021-08-01)

Correlation between high-resolution computed tomography lung nodule characteristics and EGFR mutation in lung adenocarcinomas
by: Nie Y, et al.
Published: (2019-01-01)

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer
by: Min Zhang, et al.
Published: (2020-10-01)

Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma
by: Guojin Zhang, et al.
Published: (2021-01-01)

Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT‐based radiomics signature
by: Xiaoxiao Wang, et al.
Published: (2019-10-01)

Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature
by: Jia Ran, et al.
Published: (2021-04-01)

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter
by: Lili Shi, et al.
Published: (2021-04-01)

Computed Tomography Morphological Classification of Lung Adenocarcinoma and Its Correlation with Epidermal Growth Factor Receptor Mutation Status: A Report of 1075 Cases
by: Li Q, et al.
Published: (2021-07-01)

Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC
by: Xiaofeng Li, et al.
Published: (2019-10-01)

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma
by: Guixue Liu, et al.
Published: (2021-06-01)

Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules
by: Fanyang Meng, et al.
Published: (2021-01-01)

Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma
by: Shuai Ren, et al.
Published: (2020-08-01)

Rate of EGFR mutation in patients with pulmonary adenocarcinoma
by: Waqas Shuaib, et al.
Published: (2014-12-01)

CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features
by: Linyu Wu, et al.
Published: (2020-05-01)

Radiomics Feature Activation Maps as a New Tool for Signature Interpretability
by: Diem Vuong, et al.
Published: (2020-12-01)

Relationship between EGFR mutation and computed tomography characteristics of the lung in patients with lung adenocarcinoma
by: Xiaowei Qiu, et al.
Published: (2019-02-01)

ALK and ROS1 concurrent with EGFR mutation in patients with lung adenocarcinoma
by: Mao Y, et al.
Published: (2017-07-01)

Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images
by: Lin Qi, et al.
Published: (2021-06-01)

Impact of active smoking on survival of patients with metastatic lung adenocarcinoma harboring an epidermal growth factor receptor (EGFR) mutation
by: Bulent Erdogan, et al.
Published: (2016-11-01)

Factors that Predict Clinical Benefit of EGFR TKI Therapy in Patients with EGFR Wild-Type Lung Adenocarcinoma
by: Seo Yun Kim, M.D., et al.
Published: (2019-01-01)

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes
by: Wei Zhao, et al.
Published: (2019-10-01)

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas
by: Mark Menor, et al.
Published: (2019-01-01)

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients
by: Lan Song, et al.
Published: (2020-03-01)

Impact of Radiotherapy Pattern on the Prognosis of Stage IV Lung Adenocarcinomas Harboring EGFR Mutations
by: Zhang Y, et al.
Published: (2021-04-01)

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma
by: Baihua Zhang, et al.
Published: (2021-02-01)

Conventional transbronchial needle aspiration is promising for identifying EGFR mutations in lung adenocarcinoma
by: Li‐Han Hsu, et al.
Published: (2019-04-01)

Detailed Analysis and Radiomic Prediction of First Progression Sites of First-Line Targeted Therapy for EGFR-Mutant Lung Adenocarcinoma Patients With Systemic Metastasis
by: Xiaoyang Li, et al.
Published: (2021-10-01)

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma
by: Cheng Chang, et al.
Published: (2021-03-01)

Cannot write session to /tmp/vufind_sessions/sess_essva77k14ahd8po36sovcv5hq