Predicting Severe Radiation Pneumonitis in Patients With Locally-Advanced Non-Small-Cell Lung Cancer After Thoracic Radiotherapy: Development and Validation of a Nomogram Based on the Clinical, Hematological, and Dose-Volume Histogram Parameters.
{"title":"Predicting Severe Radiation Pneumonitis in Patients With Locally-Advanced Non-Small-Cell Lung Cancer After Thoracic Radiotherapy: Development and Validation of a Nomogram Based on the Clinical, Hematological, and Dose-Volume Histogram Parameters.","authors":"Ying Zhang, Shi-Hong Zhou, Yu-Jie Yan, Lei-Lei Wu, Xiao-Shuai Yuan, Min Hu, Jing-Jing Kang, Chen-Xue Jiang, Yao-Yao Zhu, Shuang-Yan Yang, Rui-Feng Zhao, Jian Hu, Min-Ren Hu, Hui Liu, Liang Liu, Lan Zhao, Ya-Ping Xu","doi":"10.1016/j.cllc.2025.02.009","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the risk factors for severe radiation pneumonitis (RP) after thoracic radiotherapy (RT) in patients with locally advanced non-small cell lung cancer (NSCLC), develop a prediction model to identify high-risk groups, and investigate the impact of severe RP on overall survival (OS).</p><p><strong>Methods: </strong>We retrospectively collected clinical, dosimetric, and hematological factors of patients with stage III NSCLC receiving thoracic RT without immunotherapy. The primary and secondary end points were severe RP and OS, respectively. Fine-Gray competing risk regression analyses were used to identify the risk factors for severe RP. The patients were randomly divided into training and validation cohorts at a ratio of 6:4. The model was evaluated using receiver operating characteristic (ROC) and calibration curves, and decision curve analysis (DCA). The OS of patients in the RP vs. non-RP and mild RP vs. severe RP groups was analyzed using the Kaplan-Meier method.</p><p><strong>Results: </strong>A total of 305 patients were enrolled in the analysis, and 32 (10.5%) developed severe RP. Interstitial lung disease (ILD) (P = .013), percentage of ipsilateral lung volume receiving ≥ 20 Gy (ipsilateral V<sub>20</sub>) (P = .029), pre-RT derived neutrophil lymphocyte ratio (dNLR) (P = .026), and post-RT systemic inflammation response index (SIRI) (P = .010) were independent predictors of severe RP, and were used to establish the nomogram based on a training cohort. The ROC area under the curve (AUC) of the nomogram was 0.804. Calibration curves and DCA showed favorable consistency and positive net benefits in both training and validation cohorts. Cases who developed severe RP had a shorter OS than those who developed mild RP (P = .027).</p><p><strong>Conclusion: </strong>ILD, ipsilateral V<sub>20</sub>, pre-RT dNLR, and post-RT SIRI could predict severe RP in patients with locally advanced NSCLC receiving thoracic RT. By combining these indicators, a nomogram was constructed and validated, demonstrating its potential value in clinical practice.</p>","PeriodicalId":10490,"journal":{"name":"Clinical lung cancer","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical lung cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.cllc.2025.02.009","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: This study aimed to investigate the risk factors for severe radiation pneumonitis (RP) after thoracic radiotherapy (RT) in patients with locally advanced non-small cell lung cancer (NSCLC), develop a prediction model to identify high-risk groups, and investigate the impact of severe RP on overall survival (OS).
Methods: We retrospectively collected clinical, dosimetric, and hematological factors of patients with stage III NSCLC receiving thoracic RT without immunotherapy. The primary and secondary end points were severe RP and OS, respectively. Fine-Gray competing risk regression analyses were used to identify the risk factors for severe RP. The patients were randomly divided into training and validation cohorts at a ratio of 6:4. The model was evaluated using receiver operating characteristic (ROC) and calibration curves, and decision curve analysis (DCA). The OS of patients in the RP vs. non-RP and mild RP vs. severe RP groups was analyzed using the Kaplan-Meier method.
Results: A total of 305 patients were enrolled in the analysis, and 32 (10.5%) developed severe RP. Interstitial lung disease (ILD) (P = .013), percentage of ipsilateral lung volume receiving ≥ 20 Gy (ipsilateral V20) (P = .029), pre-RT derived neutrophil lymphocyte ratio (dNLR) (P = .026), and post-RT systemic inflammation response index (SIRI) (P = .010) were independent predictors of severe RP, and were used to establish the nomogram based on a training cohort. The ROC area under the curve (AUC) of the nomogram was 0.804. Calibration curves and DCA showed favorable consistency and positive net benefits in both training and validation cohorts. Cases who developed severe RP had a shorter OS than those who developed mild RP (P = .027).
Conclusion: ILD, ipsilateral V20, pre-RT dNLR, and post-RT SIRI could predict severe RP in patients with locally advanced NSCLC receiving thoracic RT. By combining these indicators, a nomogram was constructed and validated, demonstrating its potential value in clinical practice.
期刊介绍:
Clinical Lung Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of lung cancer. Clinical Lung Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of lung cancer. The main emphasis is on recent scientific developments in all areas related to lung cancer. Specific areas of interest include clinical research and mechanistic approaches; drug sensitivity and resistance; gene and antisense therapy; pathology, markers, and prognostic indicators; chemoprevention strategies; multimodality therapy; and integration of various approaches.