Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

Journal: The British journal of radiology
Published Date:

Abstract

OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients.

Authors

  • Xiaoying Pan
    School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, PR China.
  • Ting Zhang
    Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, Beijing 100020, China.
  • QingPing Yang
    School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, PR China.
  • Di Yang
    Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • Jean-Claude Rwigema
    Dept. of Radiation Oncology, MAYO CLINIC COLLEGE OF MEDICINE AND SCIENCE ARIZONA, Phoenix, AZ, United States.
  • X Sharon Qi