Background and purpose: Mandibular osteoradionecrosis (ORN) is a severe side effect affecting patients undergoing radiation therapy for head and neck cancer. Variations in the bone's vascularization and composition across the mandible may influence the susceptibility to ORN. Recently, deep learning-based models have been introduced for predicting mandibular ORN using radiation dose distribution maps to incorporate spatial information. These studies, however, only feature internal validation on a holdout subset of the data used for training. Methods and materials: This study externally validated a 3D DenseNet-40 (DN40) ORN prediction model on an independent dataset. Model performance was evaluated in terms of discrimination and calibration, with Platt scaling applied for improved external calibration. The DN40 model's discriminative ability on the external dataset was compared to a Random Forest model on corresponding dose-volume histogram (DVH) data. Results: The overall model performance was worse at external validation than at internal validation, with Platt scaling improving balance between recall and specificity but not significantly improving the overall calibration. Although the discrimination ability of the DN40 model was slightly lower at external validation (AUROC 0.63 vs. 0.69), this was statistically comparable to that of a DVH-based RF model for the same dataset (p-value 0.667). Conclusion: Our results suggest that, in addition to potential model overfitting issues, dosimetric data distribution differences between the two datasets could explain the low generalisability of the DN40 ORN prediction model. Future work will involve a larger and more diverse cohort.

The authors have declared no competing interest.

This work was supported by the Radiation Research Unit at the Cancer Research UK City of London Centre Award [C7893/A28990] and by the Guys Cancer Charity via a donation from the Wilson-Olegario foundation and other donations.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The GSTT research database Guy's Cancer Cohort) used in this study received ethics approval from the North West - Haydock Research Ethics Committee of the NHS Health Research Authority (REC reference 18/NW/0297, IRAS project ID: 231443).

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All data produced in the present study are available upon reasonable request to the authors