ArticleRadiation oncology (London, England)2026
Development of a multifactorial prediction model for symptomatic radiation pneumonitis in lung cancer patients undergoing stereotactic body radiotherapy.
Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSymptomatic radiation pneumonitis (SRP) remains a clinically significant toxicity following lung stereotactic body radiotherapy (SBRT). However, existing prediction models have seen limited clinical adoption, as they tend to be either too complex for routine application or too simplistic for reliable risk stratification. This study aimed to develop a robust prediction model by integrating key dosimetric, volumetric, and clinical variables, and to translate this model into a practical risk score to support individualized pretreatment assessment. MATERIALS AND
methodsThis retrospective analysis included 120 patients with primary lung cancer treated with SBRT between January 2019 and December 2024. A total of 24 demographic, clinical, volumetric, and dosimetric parameters were evaluated. The primary endpoint was the development of SRP. A three-step statistical framework was employed for model development. First, candidate predictors were identified using univariate analysis (Mann-Whitney U test for continuous variables and Chi-square test for categorical variables), with statistical significance defined as p < 0.05. Second, this set of variables was refined through multicollinearity diagnosis using Spearman correlation and variance inflation factor (VIF) analysis to ensure predictor independence. Third, an optimal multivariate logistic regression model was constructed, incorporating the synthetic minority over-sampling technique (SMOTE) to address class imbalance, and validated using 5-fold cross-validation and an independent test set. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, calibration plots, and the Brier score. A practical risk stratification system was subsequently developed based on the model's predicted probabilities.
resultsThe overall incidence of SRP was 43.3% (52/120). Univariate analysis identified 12 variables significantly associated with SRP (p < 0.05), encompassing all dose-volume histogram (DVH) parameters (V
conclusionThe integration of a low-dose parameter (V
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.