Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

Journal: Cancer immunology, immunotherapy : CII
Published Date: Jun 4, 2024

Abstract

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC).

Authors

  • Weimin Caii
    Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
  • Xiao Wu
  • Kun Guo
    Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Yongxian Chen
    Department of Chest Cancer, Xiamen Second People's Hospital, Xiamen, 36100, China.
  • Yubo Shi
    Department of Pulmonary, Yueqing People's Hospital, Wenzhou, 325000, China.
  • Junkai Chen
    Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China. jkcwz96@126.com.

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