# Risk prediction model for radiation pneumonitis in breast cancer radiotherapy based on dose–volume parameters combined with the neutrophil-to-lymphocyte ratio

## TL;DR

This study creates a model to predict lung complications after breast cancer radiotherapy by combining radiation dose data with blood cell ratios.

## Contribution

A novel risk prediction model for radiation pneumonitis combining V40 dose-volume parameters and pre-radiotherapy neutrophil-to-lymphocyte ratio.

## Key findings

- The combined model achieved an AUC of 0.816, significantly outperforming single indicators.
- V40 ≥ 10.45% and pre-radiotherapy NLR ≥ 2.82 were identified as independent risk factors for radiation pneumonitis.
- Regional nodal irradiation also increased the risk of radiation pneumonitis.

## Abstract

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 individualized treatment strategies.

This retrospective cohort study analyzed 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.

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 significant predictive value for RP (AUC = 0.685, 95% CI: 0.605–0.765, cutoff value: 2.82). Multivariate analysis identified independent risk factors for RP: V40 ≥ 10.45% (odds ratio [OR] = 3.24, 95% CI: 1.78–5.89, P < 0.001), pre-radiotherapy NLR ≥ 2.82 (OR = 2.56, 95% CI: 1.42–4.61, P = 0.002) and regional nodal irradiation (OR = 2.13, 95% CI: 1.18–3.84, P = 0.012). The combined prediction model achieved an AUC of 0.816 (95% CI: 0.748–0.884), significantly outperforming single indicators (ΔAUC = 0.088–0.131, P < 0.05). Bootstrap internal validation demonstrated robust model stability (C-index = 0.803).

The integrated prediction model combining V40 and the NLR effectively identifies patients a high risk of RP following breast cancer radiotherapy, facilitating personalized treatment planning and early intervention strategies.