Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal: Journal of thoracic imaging

PMID: 39319553

Abstract

PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities to identify patients who are suitable for TKI-targeted therapy and those with poor prognoses.

Authors

Xiao-Yan Wang

Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian.
Shao-Hong Wu

Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian.
Jiao Ren

Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian.
Yan Zeng

Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Li-Li Guo

Department of Radiology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian.

Keywords

Adenocarcinoma of Lung Adult Aged Deep Learning ErbB Receptors Female Humans Lung Lung Neoplasms Male Middle Aged Mutation Radiomics Reproducibility of Results Retrospective Studies Sensitivity and Specificity Tomography, X-Ray Computed Tumor Suppressor Protein p53

External Resources

View on PubMed Access via DOI PubMed (39319553)

Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Abstract

Authors

Keywords

External Resources

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