Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
October 15, 2026 · via Europe PMC
Patient selection for proton therapy in lung cancer: Updating and internal-external cross-validation of a normal tissue complication probability model for acute oesophageal toxicity.
Wijsman R, van Loon JGM, Bartels R, +5 more, Schuit E
AI summary — for doctors, generated from the abstract only
An updated model using mean oesophagus dose and overall treatment time predicts acute oesophageal toxicity in lung radiotherapy and now supports proton therapy selection.
What was studied
External validation and internal-external cross-validation of an NTCP model for grade 2 or higher acute oesophageal toxicity using 937 patients from four centres.
Key findings
- 47% of patients had grade 2 or higher toxicity.
- Concurrent chemotherapy, gender and stage were dropped and overall treatment time added, leaving mean oesophagus dose and treatment time.
- The final model had optimism-corrected AUC 0.75 and low heterogeneity across centres.
Clinical relevance
Radiation oncologists can use the model to decide which lung cancer patients gain most from proton therapy.
Who should care
Limitations
Discrimination is moderate (AUC 0.75), the calibration slope of 1.26 suggests imperfect calibration, and the data are retrospective cohorts from a limited number of centres.
Use the model as one input when selecting patients for protons, keeping its moderate accuracy in mind.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
PURPOSE
To externally validate and update a published normal tissue complication probability (NTCP) model for the prediction of Grade ≥ 2 acute oesophageal toxicity (AOT) in lung cancer patients receiving (chemo-)radiotherapy (predictors: concurrent chemotherapy, gender, tumour stage, mean oesophagus dose (MOD)).
METHODS AND MATERIALS
Patient cohorts from four centres were available. Initial external validation was performed by assessing model performance. Heterogeneity of performance across cohorts was evaluated using an internal-external cross validation (IECV) method. Based on the IECV procedure, parameters were added or removed from the model to lower heterogeneity in model performance across cohorts with the aim to create a model that performed uniformly well across centres. Ridge regression was used to create the final model and bootstrapping techniques were applied to perform further internal validation.
RESULTS
Out of 937 patients, 444 (47%) had Grade ≥ 2 AOT. External validation indicated model recalibration was necessary for 1 cohort and model revision for another. IECV showed heterogeneity of performance of the original model across cohorts. To lower heterogeneity in model performance, the predictors concurrent chemotherapy, gender, and tumour stage were removed from the model, and overall treatment time (OTT) was added. The final model containing MOD and OTT as predictors showed low levels of heterogeneity in model performance (Iof 0% for all performance measures) with optimism-corrected AUC of 0.75, calibration intercept of -0.01 and slope of 1.26.
CONCLUSION
The validation and updating of an NTCP-model for AOT resulted in a prediction model performing well in varying patient cohorts. Therefore, this updated model was added to the national indication protocol to select lung cancer patients for proton radiotherapy.
DOI
10.1016/j.radonc.2026.111823
PMID
42880415
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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