ArticleAnnals of medicine2026
Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Immune Checkpoint Inhibitor-Induced Pneumonitis in Non-Small Cell Lung Cancer: A Narrative Review of Incidence and Clinical Risk Factors.Current oncology (Toronto, Ont.) · 2026Review
- Time-of-day radiotherapy alters radiation pneumonitis risk but not survival in lung cancer patients with interstitial lung disease.Therapeutic advances in medical oncology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But predictive models integrating multimodal data are lacking. This study aimed to develop a novel machine learning-based nomogram integrating clinical, dosimetric, and inflammatory predictors for RP risk assessment in this high-risk population.
methodsThis retrospective study of 424 ILD patients collected clinical, dosimetric, and inflammatory data. Machine learning algorithms created composite dosimetric (
resultsRP occurred in 200 (47%) patients. Independent risk factors included higher performance status, Charlson comorbidity index (CCI), usual interstitial pneumonia (UIP) pattern, immunotherapy, concurrent chemoradiotherapy, more radiation sessions, and lower lung volume. The
conclusionThe integrated nomogram combining clinical, dosimetric, and inflammatory predictors enables accurate, individualized RP risk assessment in lung cancer patients with ILD. It can guide adjustments to individualized radiotherapy plans or preventive interventions, supporting better patient selection, treatment decisions, and proactive follow-up.
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