ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1684629
This article is part of the Research TopicRecent developments in artificial intelligence and radiomicsView all 9 articles
Predicting Radiation Pneumonitis with Dose-segmented Radiomics in Locally Advanced Non-Small Cell Lung Cancer Patients Undergoing Consolidative Immunotherapy Post-Concurrent Chemoradiotherapy
Provisionally accepted
- 1The First Affiliated Hospital of Shantou University Medical College, Shantou, China
- 2Shandong First Medical University Cancer Hospital, Jinan, China
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Objective: To develop and validate a machine learning model that integrates dose distribution-based radiomics, clinicopathological parameters, and hematological inflammatory biomarkers for predicting radiation pneumonitis (RP) in locally advanced non-small cell lung cancer (LA-NSCLC) patients receiving immuno-consolidation therapy after concurrent chemoradiation (CCRT). Methods: This retrospective study analyzed 161 locally advanced non-small cell lung cancer (LA-NSCLC) patients divided into training (n=112) and validation (n=49) cohorts. Radiomics features were extracted from planning CT scans across nine 5-Gy dose gradients (0-60 Gy), including the initial positioning CT (before radiotherapy) and a resetting CT (after a cumulative dose of 40-50 Gy), all within regions of interest (ROIs). Longitudinal feature changes were analyzed, followed by LASSO-based feature selection and logistic regression modeling. Machine learning methods evaluated associations between radiomics signatures (RS), clinical features, hematological inflammatory markers, and RP. Model performance was evaluated with AUC metrics and decision curve analysis (DCA). Results: Radiomics signatures across dose ranges (RS1:5 Gy; RS3:10-15 Gy; RS4:15-20 Gy; RS5:20-30 Gy; RS7:40-50 Gy; RS8:50-55 Gy; RS9:55-60 Gy) were developed. RS8 demonstrated the highest validation AUC (0.854). The model based on RS8 combined with tumor location achieved an AUC of 0.918 in the training cohort for predicting RP, whereas the addition of the neutrophil-to-lymphocyte ratio at 4 week (NLR 4w) to this model resulted in a marginally higher AUC of 0.938. Conclusions: The combined model improves RP prediction in LA-NSCLC patients undergoing post-CCRT consolidative immunotherapy, offering a novel approach for personalized patient management.
Keywords: Radiation Pneumonitis, LA-NSCLC, consolidative immunotherapy, CT radiomics, machine learning
Received: 12 Aug 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Wang, Wang, Wang, Yu and Meng. 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: Xue Meng, mengxuesdzl@163.com
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