Skip to main content

REVIEW article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1434171
This article is part of the Research Topic Investigating tumor immunotherapy responses in lung cancer using deep learning View all 3 articles

Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer

Provisionally accepted
Jie Zheng Jie Zheng 1Shuang Xu Shuang Xu 2Guoyu Wang Guoyu Wang 1Yiming Shi Yiming Shi 1*
  • 1 Taizhou University, Taizhou, China
  • 2 Oxfam Australia, Melbourne, Victoria, Australia

The final, formatted version of the article will be published soon.

    In recent years, there has been significant research interest in the field of immunotherapy for non-small cell lung cancer (NSCLC) within the academic community. Given the observed variations in individual responses, despite similarities in histopathologic type, immunohistochemical index, TNM stage, or mutation status, the identification of a reliable biomarker for early prediction of therapeutic responses is of utmost importance. Conventional medical imaging techniques primarily focus on macroscopic tumor monitoring, which may no longer adequately fulfill the requirements of clinical diagnosis and treatment. CT (computerized tomography) or PEF/CT-based radiomics has the potential to investigate the molecular-level biological attributes of tumors, such as PD-1/PD-L1 expression and tumor mutation burden, which offers a novel approach to assess the effectiveness of immunotherapy and forecast patient prognosis. The utilization of cutting-edge radiological imaging techniques, including radiomics, PET/CT, machine learning, and artificial intelligence, demonstrates significant potential in predicting diagnosis, treatment response, immunosuppressive characteristics, and immune-related adverse events. The current review highlights that CT scan-based radiomics is a reliable and feasible way to predict the benefits of immunotherapy in patients with advanced NSCLC.

    Keywords: Non-small cell lung cancer, Radiomics, CT, Immunotherapy, Immune checkpoint

    Received: 17 May 2024; Accepted: 05 Aug 2024.

    Copyright: © 2024 Zheng, Xu, Wang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yiming Shi, Taizhou University, Taizhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.