A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

Journal: International journal of radiation oncology, biology, physics

PMID: 31201897

Abstract

PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity.

Authors

Kuo Men

State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China.
Huaizhi Geng
Haoyu Zhong
Yong Fan

CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
Alexander Lin

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, United States.
Ying Xiao

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.

Keywords

Area Under Curve Deep Learning Humans Hypopharyngeal Neoplasms Laryngeal Neoplasms Logistic Models Oropharyngeal Neoplasms Parotid Gland Pharyngeal Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Radiotherapy, Image-Guided Retrospective Studies ROC Curve Squamous Cell Carcinoma of Head and Neck Submandibular Gland Tomography, X-Ray Computed Xerostomia

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View on PubMed Access via DOI PubMed (31201897)

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

Abstract

Authors

Keywords

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