Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.

Journal: Journal of cancer research and clinical oncology
Published Date: Jan 19, 2023

Abstract

PURPOSE: We analyzed clinical features and the representative HE-stained pathologic images to predict 5-year overall survival via the deep-learning approach in cervical cancer patients in order to assist oncologists in designing the optimal treatment strategies.

Authors

  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Kui Sun
    Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Road, No. 324, Jinan, 250021, China.
  • Caiyi Zhang
    Department of Obstetrics and Gynecology, Maternal and Child Health Hospital of Hunan Province, Changsha, 410008, China.
  • Kang Ren
    Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
  • Chao Li
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Lin Shen
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Di Jing
    Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China. jingdi2222@gmail.com.

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