Article
Biotechnology & Applied Microbiology
Quan Chen, Yan Li, Qiguang Cheng, Juno Van Valkenburgh, Xiaotian Sun, Chuansheng Zheng, Ruiguang Zhang, Rong Yuan
Summary: This study establishes a non-invasive tool to predict EGFR mutation status and subtypes of lung adenocarcinoma based on radiomic features extracted from CT images. By selecting appropriate features, prediction models are built, and the integrated model demonstrates the best performance in predicting EGFR mutation status and subtypes.
ONCOTARGETS AND THERAPY
(2022)
Article
Oncology
Guojin Zhang, Yuntai Cao, Jing Zhang, Zhiyong Zhao, Wenjuan Zhang, Junlin Zhou
Summary: The study analyzed the relationship between dual-energy spectral CT and EGFR mutation status in patients with lung adenocarcinoma. Significant differences were observed in sex and smoking history according to EGFR mutation status. The quantitative parameters of spectral CT showed significant differences between EGFR mutant and wild-type groups, with the combination of these parameters providing the best diagnostic performance for determining EGFR mutation status.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2021)
Article
Oncology
Ji-wen Huo, Tian-you Luo, Le Diao, Fa-jin Lv, Wei-dao Chen, Rui-ze Yu, Qi Li
Summary: This study shows that combined models incorporating radiomics signatures, clinical, and CT morphological features can help predict EGFR-mutation subtypes in lung adenocarcinoma, contributing to individualized treatment for patients.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Guojin Zhang, Jing Zhang, Yuntai Cao, Zhiyong Zhao, Shenglin Li, Liangna Deng, Junlin Zhou
Summary: This study successfully developed and validated a nomogram for predicting EGFR mutation status in patients with lung adenocarcinoma, combining CT features and clinical risk factors, offering a convenient and non-invasive method before surgery.
TRANSLATIONAL ONCOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yue Guo, Hui Zhu, Congxia Chen, Xu Li, Fugeng Liu, Zhiming Yao
Summary: This study established a prediction model based on imaging data and clinical features to predict the EGFR mutation status in patients with LADC. The model showed good predictive efficacy and accuracy.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2022)
Article
Oncology
Guotao Yin, Ziyang Wang, Yingchao Song, Xiaofeng Li, Yiwen Chen, Lei Zhu, Qian Su, Dong Dai, Wengui Xu
Summary: This study developed a deep learning system to predict EGFR mutant lung adenocarcinoma in F-18-FDG PET/CT images. The stacking model showed potential in predicting EGFR mutation status non-invasively and may assist clinicians in identifying suitable patients for EGFR-targeted therapy.
FRONTIERS IN ONCOLOGY
(2021)
Article
Oncology
Hyun Jung Yoon, Jieun Choi, Eunjin Kim, Sang-Won Um, Noeul Kang, Wook Kim, Geena Kim, Hyunjin Park, Ho Yun Lee
Summary: A deep learning model based on CT images combined with clinical factors can predict the EGFR mutation status in patients with pure ground-glass opacity nodule (pGGN) lung adenocarcinoma, and its clinical utility has been demonstrated in a real-world sample.
FRONTIERS IN ONCOLOGY
(2022)
Article
Medicine, General & Internal
Qi Li, Xiao-Qun He, Xiao Fan, Tian-You Luo, Ji-Wen Huo, Xing-Tao Huang
Summary: This study identified eight morphological types of lung adenocarcinoma based on CT imaging, with different frequencies of EGFR mutations observed among these types. Understanding the correlation between tumor morphology and EGFR mutation status may aid in accurate diagnosis and treatment decision-making for LADC patients.
INTERNATIONAL JOURNAL OF GENERAL MEDICINE
(2021)
Article
Oncology
Meilin Jiang, Pei Yang, Jing Li, Wenying Peng, Xingxiang Pu, Bolin Chen, Jia Li, Jingyi Wang, Lin Wu
Summary: Radiomics signature can be used to predict EGFR mutation status, and skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Hong-Yue Zhao, Ye-Xin Su, Lin-Han Zhang, Peng Fu
Summary: The aim of this study was to develop a prediction model for EGFR mutations in lung adenocarcinoma. Based on PET/CT scans and genetic testing, radiomic features and clinical factors were used to build several prediction models. Model-4, which combined radiomic features and clinical factors, showed the best performance in identifying EGFR mutations.
Article
Oncology
Runping Hou, Xiaoyang Li, Junfeng Xiong, Tianle Shen, Wen Yu, Lawrence H. H. Schwartz, Binsheng Zhao, Jun Zhao, Xiaolong Fu
Summary: The study demonstrates that three-dimensional convolutional neural networks with deep transfer learning can effectively stratify progression status in patients with EGFR mutations, aiding in clinical decision-making.
FRONTIERS IN ONCOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Bo Cheng, Hongsheng Deng, Yi Zhao, Junfeng Xiong, Peng Liang, Caichen Li, Hengrui Liang, Jiang Shi, Jianfu Li, Shan Xiong, Ting Lai, Zhuxing Chen, Jianrong Wu, Tianyi Qian, Wenjing Huan, Man Tat Alexander Ng, Jianxing He, Wenhua Liang
Summary: This study established a non-invasive radiomics model based on computed tomography (CT) to predict EGFR mutation status in GGO-featured lung adenocarcinoma, subsequently guiding the administration of targeted therapy with favorable sensitivity and specificity.
EUROPEAN RADIOLOGY
(2022)
Article
Oncology
Baihua Zhang, Shouliang Qi, Xiaohuan Pan, Chen Li, Yudong Yao, Wei Qian, Yubao Guan
Summary: The study proposed a method using deep learning models and radiomics features to recognize EGFR gene mutation status in LADC patients. Through research, it was found that the SE-CNN model can accurately identify EGFR status, with performance significantly outperforming other models.
FRONTIERS IN ONCOLOGY
(2021)
Review
Oncology
Xinyu Ge, Jianxiong Gao, Rong Niu, Yunmei Shi, Xiaoliang Shao, Yuetao Wang, Xiaonan Shao
Summary: This article reviews the progress in applying 18F-FDG PET/CT and radiomics in clinical research on lung adenocarcinoma and how these data are analyzed using traditional statistics, machine learning, and deep learning to predict EGFR mutation status. Satisfactory results have been achieved through traditional statistics, machine learning, and deep learning. Future research should combine these methods to achieve more accurate predictions and provide reliable evidence for the precision treatment of lung adenocarcinoma.
FRONTIERS IN ONCOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hong-fan Liao, Xing-tao Huang, Xian Li, Fa-jin Lv, Tian-you Luo, Qi Li
Summary: This study investigated the dynamic changes during follow-up CT, histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC). The results showed that there were differences in growth rate, histological subtypes, gene mutation status, and surgical prognosis among different morphological types of SLADC. Therefore, a good understanding of different morphological types of SLADC is important for accurate diagnosis, individualized treatment strategies, and predicting patient outcomes.
INSIGHTS INTO IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Chul-min Lee, Soon-Young Song, Seok Chol Jeon, Choong-Ki Park, Yo Won Choi, Youkyung Lee
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY
(2016)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yoonah Song, Yo Won Choi, Seung Sam Paik, Dae Hee Han, Kyo Young Lee
EUROPEAN JOURNAL OF RADIOLOGY
(2017)
Article
Anatomy & Morphology
Seunghun Lee, Yo Won Choi, Seok Chol Jeon
Article
Radiology, Nuclear Medicine & Medical Imaging
Yo Won Choi, Jo-Anne O. Shepard, Jinoo Kim, Seok Chol Jeon, Choong Ki Park, Jeong-Nam Heo, Doo Jin Paik
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY
(2011)
Article
Radiology, Nuclear Medicine & Medical Imaging
Eun Ju Lee, Yo Won Choi, Seok Chol Jeon, Jeong-Nam Heo, Choong Ki Park, Seung Sam Paik, Won Sang Chung
JOURNAL OF THORACIC IMAGING
(2011)
Article
Radiology, Nuclear Medicine & Medical Imaging
Il Soo Chang, Min Woo Lee, Young Il Kim, Seung Hong Choi, Hyo-Cheol Kim, Yo Won Choi, Chang Jin Yoon, Sung Wook Shin, Hyo K. Lim
JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY
(2011)
Article
Medicine, General & Internal
Hyun Jung Kwak, Ji-Yong Moon, Yo Won Choi, Tae Hyung Kim, Jang Won Sohn, Ho Joo Yoon, Dong Ho Shin, Sung Soo Park, Sang-Heon Kim
TOHOKU JOURNAL OF EXPERIMENTAL MEDICINE
(2010)
Article
Respiratory System
Dong Won Park, Tae Hyung Kim, Jang Won Shon, Ho Joo Yoon, Sung Soo Park, Seok Chol Jeon, Yo Won Choi, Seung Sam Paik, Hyuck Kim
SARCOIDOSIS VASCULITIS AND DIFFUSE LUNG DISEASES
(2015)
Article
Radiology, Nuclear Medicine & Medical Imaging
JM Ko, JI Jung, SH Park, KY Lee, MH Chung, MI Ahn, KJ Kim, YW Choi, ST Hahn
AMERICAN JOURNAL OF ROENTGENOLOGY
(2006)
Article
Radiology, Nuclear Medicine & Medical Imaging
M Jang, YW Choi, SC Jeon, C Park, HJ Yoon
CLINICAL RADIOLOGY
(2006)
Article
Radiology, Nuclear Medicine & Medical Imaging
BS Kim, IK Hwang, YW Choi, S Namkumg, HC Kim, WC Hwang, KM Choi, JK Park, T Il Han, WC Kang
Review
Respiratory System
Yo Won Choi
TUBERCULOSIS AND RESPIRATORY DISEASES
(2005)
Article
Radiology, Nuclear Medicine & Medical Imaging
JN Heo, YW Choi, SC Jeon, CK Park
AMERICAN JOURNAL OF ROENTGENOLOGY
(2005)
Review
Radiology, Nuclear Medicine & Medical Imaging
YR Lee, YW Choi, KJ Lee, SC Jeon, CK Park, JN Heo
BRITISH JOURNAL OF RADIOLOGY
(2005)