Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer.

Journal: Clinical radiology
Published Date: Jan 14, 2023

Abstract

AIM: To develop and validate a nomogram model that combines computed tomography (CT)-based radiological factors extracted from deep-learning and clinical factors for the early predictions of immune checkpoint inhibitor-related pneumonitis (ICI-P).

Authors

  • M Cheng
    Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • R Lin
    College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
  • N Bai
    College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
  • Y Zhang
    University Technology Sydney, 15 Broadway, Ultimo, NSW Australia.
  • H Wang
    Department of Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
  • M Guo
    Department of Internal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • X Duan
  • J Zheng
    Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
  • Z Qiu
    College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China. Electronic address: qiuzw@nefu.edu.cn.
  • Y Zhao
    Department of Orthopedics, Yantai Shan Hospital, Yantai 264008, China.

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