Abstract
Conventional and existing deep learning (DL) normal tissue complication probability (NTCP) models for head and neck cancer (HNC) typically predict a single radiation-induced toxicity at a time and provide only deterministic risk estimates, despite evidence that toxicities share biological mechanisms, and despite a clinical need for information about the reliability of these risk estimates. Thus, this study proposes a multi-toxicity (MT) deep learning model that jointly predicts five toxicity endpoints (aspiration, dysphagia, sticky saliva, taste alteration, and xerostomia) and evaluates two uncertainty quantification (UQ) methods, Monte Carlo dropout (MCD) and deep ensembles (DE). Using a cohort of 1,373 HNC patients, the MT model achieved comparable performance to single-toxicity (ST) models and higher discriminative performance and calibration for sticky saliva and taste alteration. For UQ, both DE and MCD produced reliable uncertainty estimates when combined with binary entropy, with DE consistently outperforming MCD. These findings demonstrate that MT modelling can provide a more comprehensive and better-calibrated toxicity risk profile than ST models, and that DE combined with binary entropy offers the most reliable basis for uncertainty-aware, trustworthy DL-based NTCP modelling.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_007.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{MacDan_More_MICCAISAT2026,
author = { MacRae, Daniel C. AND van der Hoek, Luuk AND de Vette, Suzanne P. M. AND Neh, Hendrike AND van Aalst, Joëlle E. AND Valdenegro-Toro, Matias AND Sijtsema, Nanna M. AND van Ooijen, Peter M. A. AND van Dijk, Lisanne V.},
title = { { More Comprehensive and Reliable Deep Learning Normal Tissue Complication Probability Models for Head and Neck Cancer Patients } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}