ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Risk Prediction Model for Radiation Pneumonitis in Breast Cancer Radiotherapy Based on Dose–Volume Parameters Combined with the Neutrophil-to-Lymphocyte Ratio
Provisionally accepted
- 1The First Affiliated Hospital of Ningbo University, Ningbo, China
- 2Ningbo University, Ningbo, China
- 3Ningbo First Hospital, Ningbo, China
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Purpose: To develop and validate a risk prediction model for radiation pneumonitis (RP) and radiation-induced pulmonary fibrosis (RIPF) following breast cancer radiotherapy by integrating the V40 dose–volume parameter with the neutrophil-to-lymphocyte ratio (NLR), providing guidance for individualised treatment strategies. Methods: This retrospective cohort study analysed clinical data from 164 patients with breast cancer who underwent postoperative radiotherapy between May 2018 and August 2020. Clinical–pathological characteristics, radiotherapy dosimetric parameters and NLR values were collected at three time points: pre-surgery, 1 week before radiotherapy and 1 month after radiotherapy. Radiation pneumonitis (0–6 months) and RIPF (≥6 months) were assessed according to the Common Terminology Criteria for Adverse Events (version 5.0). Receiver operating characteristic (ROC) curves were used to determine the optimal predictive indicators. Variable selection was performed using least absolute shrinkage and selection operator regression followed by multivariate logistic regression to construct the prediction model. Internal validation was conducted using 1,000 bootstrap resampling iterations. Results: Of the 164 patients, 107 (65.2%) developed varying degrees of RP (grade 1: n = 103, 62.8%; grade 2: n = 4, 2.4%), and 118 (72.0%) developed RIPF (all grade 1). The ROC analysis revealed that ipsilateral lung V40 had superior predictive performance for RIPF (area under the curve [AUC] = 0.728, 95% confidence interval [CI]: 0.651–0.805, cutoff value: 10.45%). The pre-radiotherapy NLR showed
Keywords: breast cancer, Neutrophil lymphocyte ratio (NLR), predictive models, Radiation Pneumonitis, Risk factors
Received: 06 Nov 2025; Accepted: 09 Feb 2026.
Copyright: © 2026 Zhou, Liu, Lou, Yang, Xu, Dai, Lan and Ren. 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:
Jianliang Zhou
Jiangping Ren
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