Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

Journal: BMC medical imaging
Published Date:

Abstract

BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep learning to predict durable clinical benefits from immunotherapy.

Authors

  • Xiaoxiao Huang
    Department of medical laboratory, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang province, PR China.
  • Xiaoxin Huang
    School of Finance and Trade, Wenzhou Business College, Wenzhou, Zhejiang 325035, China.
  • Yurun Xie
    Department of Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
  • Kui Wang
    The Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
  • Housheng Bai
    Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
  • Ruiling Ning
    Department of Lung Cancer, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China.
  • Xiqi Zhu
    Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, 533000, Baise, China (Q.W., C.H., J.Z., Z.Z., X.Z.); Life Science and clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, 533000, Baise, China (C.H., X.Z.). Electronic address: xiqi.zhu@163.com.
  • Deyou Huang
    Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China.
  • Guanqiao Jin
    Department of Radiology, Guangxi Medical University Cancer Hospital, Guangxi Clinical Research Center for Imaging Medicine, Guangxi Clinical Key Specialty (Medical Imaging), Key Discipline Development Program (Medical Imaging), Affiliated Cancer Hospital of Guangxi Medical University, Nanning, China.