Use of deep learning to predict the need for aggressive nutritional supplementation during head and neck radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Jun 1, 2022

Abstract

PURPOSE/OBJECTIVES: Radiation therapy (RT) for the treatment of patients with head and neck cancer (HNC) leads to side effects that can limit a person's oral intake. Early identification of patients who need aggressive nutrition supplementation via a feeding tube (FT) could improve outcomes. We hypothesize that traditional machine learning techniques used in combination with deep learning techniques could identify patients early during RT who will later need a FT.

Authors

  • Michael Dohopolski
    Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States of America.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Howard Morgan
    Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology,University of Texas Southwestern Medical Center, Dallas, Texas, United States.
  • David Sher
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.

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