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NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy

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Introduction: Prediction models using logistic regression may perform poorly in external patient cohorts. However, there is a need to standardize and validate models for clinical use. The purpose of this project was to describe a method for validation of external NTCP models used for patient selection in the randomized trial of protons versus photons in head and neck cancer radiotherapy, DAHANCA 35. Material and methods: Organs at risk of 588 patients treated primarily with IMRT in the randomized controlled DAHANCA19 trial were retrospectively contoured according to recent international recommendations. Dose metrics were extracted using MatLab and all clinical parameters were retrieved from the DAHANCA database. The model proposed by Christianen et al. to predict physician-rated dysphagia was validated through the closed testing, where change of the model intercept, slope and individual beta's were tested for significant prediction improvements. Results: Six months prevalence of dysphagia in the validation cohort was 33%. The closed testing procedure for physician-rated dysphagia showed that the Christianen et al. model needed an intercept refitting for the best match for the Danish patients. The intercept update increased the risk of dysphagia for the validation cohort by 7.9 ± 2.5% point. For the raw model performance, the Brier score (mean squared residual) was 0.467, which improved significantly with a new intercept to 0.415. Conclusions: The previously published Dutch dysphagia model needed an intercept update to match the Danish patient cohort. The implementation of a closed testing procedure on the current validation cohort allows quick and efficient validation of external NTCP models for patient selection in the future.

Original languageEnglish
JournalActa oncologica
Volume58
Issue number10
Pages (from-to)1410-1415
Number of pages6
ISSN0284-186X
DOIs
Publication statusPublished - 3 Oct 2019

ID: 58596458