ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1495450

This article is part of the Research TopicArtificial Intelligence for Cancer ImmunotherapyView all 7 articles

Development and Validation of a Nomogram for Differentiating Immune Checkpoint Inhibitor-related Pneumonitis from Pneumonia in patients undergoing immunochemotherapy: A Multicenter, Real-World, Retrospective Study

Provisionally accepted
Linli  DuanLinli Duan1Guanglu  LiuGuanglu Liu2Zijie  HuangZijie Huang3Rong  ChenRong Chen1Di  MoDi Mo1Yuxiao  XiaYuxiao Xia1*Jiazhu  HuJiazhu Hu4*Mengzhang  HeMengzhang He1*
  • 1The Second Afliated Hospital, Guangzhou Medical University, Guangzhou, China
  • 2GRGBanking Equipment Company Ltd.,, Guangzhou, China
  • 3Guangzhou Institute of Cancer Research, Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China
  • 4the Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou, China

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

Background: Immune Checkpoint Inhibitor-related Pneumonitis (CIP) exhibits high morbidity and mortality rates in the real world, often coexisting with pneumonia, particularly after immunochemotherapy. We aimed to develop and validate a non-invasive nomogram for differentiating CIP from pneumonia in patients undergoing immunochemotherapy.Methods: This study encompassed 237 patients from three hospitals. A multivariate logistic regression analysis was conducted to identify risk factors for CIP. Utilizing the random forest machine learning method, optimal development and validation cohort allocation ratios (in a ratio of 8:2) were determined for the predictive model. The performance of the nomogram was evaluated using calibration, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Subsequently respiratory pathogens, management, and outcomes were compared between CIP and No CIP cases.Results: Among the 237 patients, 104 were diagnosed with CIP, and 133 were no CIP but pneumonia(No CIP). Smoking status, prior chronic obstructive pulmonary disease (COPD), ground glass opacities, non-specific interstitial pneumonitis, Neutrophil to Lymphocyte Ratio (NLR), pleural effusions, and Oxygen Partial Pressure (PaO2) emerged as non-invasive independent predictors of CIP. The nomogram exhibited good discrimination for both the development and validation cohorts, with AUC values of 0.817 (95% CI, 0.754-0.879) and 0.913 (95% CI, 0.826-0.999), respectively. The calibration curves demonstrated good fit for both the development and validation cohort, as evidenced by the Hosmer-Lemeshow tests (χ² = 3.939, p = 0.863 and χ² = 8.117, p = 0.422, respectively). DCA further highlighted their clinical utility. In CIP patients, the use of gamma globulin/albumin and glucocorticoids was significantly higher than in No CIP patients (39.4% vs 23.3%, p = 0.007; 79.8% vs 12.8%, p < 0.0001, respectively). The proportion of patients requiring mechanical ventilation was also significantly higher in the CIP compared to the No CIP group (21.2% vs 11.3%, p = 0.038).The nomogram offers a non-invasive approach to differentiate CIP from pneumonia associated with immunochemotherapy, potentially facilitating early intervention and informed treatment decisions.

Keywords: Checkpoint inhibitor-related pneumonitis (CIP), immunochemotherapy, nomogram, Differentiate, Pneumonia

Received: 12 Sep 2024; Accepted: 29 Apr 2025.

Copyright: © 2025 Duan, Liu, Huang, Chen, Mo, Xia, Hu and He. 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:
Yuxiao Xia, The Second Afliated Hospital, Guangzhou Medical University, Guangzhou, China
Jiazhu Hu, the Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou, China
Mengzhang He, The Second Afliated Hospital, Guangzhou Medical University, Guangzhou, China

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