Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.

Journal: European radiology

Published Date: Dec 1, 2020

Abstract

OBJECTIVE: To compare the CT texture feature reproducibility of 2D and 3D segmentations and their machine learning (ML)-based classifications for predicting human papilloma virus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).

Authors

Jiliang Ren

Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Ying Yuan

Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: yuany83@163.com.
Meng Qi

Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, PR China.
Xiaofeng Tao

Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: cjr.taoxiaofeng@vip.163.com.

Keywords

Algorithms Alphapapillomavirus Area Under Curve Diagnosis, Differential Female Head and Neck Neoplasms Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Machine Learning Male Observer Variation Papillomavirus Infections Regression Analysis Reproducibility of Results Retrospective Studies Squamous Cell Carcinoma of Head and Neck Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (32591885)

Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.

Abstract

Authors

Keywords

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