A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy

D.C. MacRae; L. van der Hoek; S.P.M. de Vette; H. Neh; A.C. Moreno; C.D. Fuller; J.A. Langendijk; M.A. Valdenegro-Toro; N.M. Sijtsema; PMA van Ooijen; L.V. van Dijk

doi:10.1016/j.radonc.2026.111486

March 1, 2026Radiotherapy and OncologyOpen Access

A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy

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Key Points

The aim is to develop a deep learning model that predicts multiple radiotherapy-related toxicities in head and neck cancer patients.
Developed a deep learning NTCP model incorporating 3D CT data
Analyzed dose distribution and treatment contours
Implemented multi-toxicity prediction for simultaneous toxicities
Multi-toxicity model enhances prediction accuracy for dysphagia and xerostomia
Demonstrated ability to predict interrelated toxicities
Contributes to personalized treatment approaches for patients

Implication

A multi-toxicity deep learning approach predicts toxicity in head and neck cancer, suggesting improved patient care.

Abstract

Radiotherapy-related head and neck toxicities are interrelated and co-occur. Deep learning NTCP model using 3D CT, dose distribution, and contours. One NTCP model that can predict multiple toxicities simultaneously. Multi-toxicity modelling improves dysphagia and xerostomia prediction. A step towards more comprehensive modelling for personalised patient care.

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Cite This Study

MacRae et al. (2026) studied this question.

synapsesocial.com/papers/69be34d16e48c4981c672ef4 https://doi.org/10.1016/j.radonc.2026.111486

View Full Paper

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A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy

Key Points

Implication

Abstract

Discussion

Cite This Study

A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy

Key Points

Implication

Abstract

Discussion

Cite This Study

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