Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Yang Shu; Wei Xu; Rui Su; Pancen Ran; Lei Liu; Zhizhao Zhang; Jing Zhao; Zhen Chao; Guobin Fu

doi:10.3389/fimmu.2023.1251645

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Front Immunol. 2023 Sep 20:14:1251645. doi: 10.3389/fimmu.2023.1251645. eCollection 2023.

Authors

Yang Shu^{1

2}, Wei Xu^{1

3}, Rui Su⁴, Pancen Ran^{1

2}, Lei Liu¹, Zhizhao Zhang¹, Jing Zhao¹, Zhen Chao⁴, Guobin Fu^{1

2

3

5}

Affiliations

¹ Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
² The Second Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
³ Department of Oncology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.
⁴ College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
⁵ Department of Oncology, The Third Affiliated Hospital of Shandong First Medical University, Jinan, Shandong, China.

Abstract

Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.

Keywords: deep learning; immune checkpoint inhibitor-related pneumonitis; immunotherapy; non-small cell lung cancer; radiomics.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Carcinoma, Non-Small-Cell Lung* / drug therapy
Humans
Immune Checkpoint Inhibitors / adverse effects
Immunotherapy / adverse effects
Lung Neoplasms* / drug therapy
Pneumonia* / diagnosis
Pneumonia* / diagnostic imaging

Substances

Immune Checkpoint Inhibitors

Grants and funding

Taishan Scholar Foundation of Shandong Province, Grant/Award Number: tsqn202103179; National Natural Science Foundation of China, Grant/Award Number: 81802284; Science and Technology Development Plans of Shandong Province, Grant/Award Number: 2014GSF118157; Scientific Research Foundation of Shandong Province of Outstanding Young Scientists, Grant/Award Number: BS2013YY058; 2021 Shandong Medical Association Clinical Research Fund, Grant/Award Number: YXH2022ZX02176; Natural Science Foundation of Shandong Province, Grant/Award Number: ZR2022MH088.