Evidence map›Paper›PMID 41033356›Full record

ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2025

Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations.

S P M de Vette, H Neh, L van der Hoek, D C MacRae, H Chu, A Gawryszuk, R J H M Steenbakkers, P M A van Ooijen, C D Fuller, K A Hutcheson and 3 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

S P M de VetteDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Electronic address: s.p.m.de.vette@umcg.nl.
H NehDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
L van der HoekDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
D C MacRaeDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
H ChuDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
A GawryszukDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
R J H M SteenbakkersDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
P M A van OoijenDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
C D FullerDepartment of Radiation Oncology, the University of Texas MD Anderson Cancer Center, Houston, USA.
K A HutchesonDepartment of Radiation Oncology, the University of Texas MD Anderson Cancer Center, Houston, USA; Department of Head and Neck Surgery, the University of Texas MD Anderson Cancer Center, Houston, TX, USA.
J A LangendijkDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
N M SijtsemaDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
L V van DijkDepartment of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Electronic address: l.v.van.dijk@umcg.nl.

Funding

Project 3: OPC-RADP01CA285249 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Clifton David Fuller, Katherine Arnold Hutcheson · 2024 to 2026
$9.4M
Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac deviceR01DE028290 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHRISTODOULEAS, JOHN PAUL, FULLER, CLIFTON DAVID · 2019 to 2023
$4.3M
Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer TherapyR01CA258827 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2021 to 2025
$2.9M
Dissemination and implementation of DIGEST™ as an evidence-based measurement tool for dysphagia in cancerR01CA271223 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Katherine Arnold Hutcheson · 2023 to 2026
$2.6M
SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck CancerR01CA257814 · NCI · RICE UNIVERSITY · PI FULLER, CLIFTON DAVID, SCHAEFER, ANDREW J · 2021 to 2024
$1.8M
Hypoglossal neuropathy in the pathogenesis of radiation associated dysphagia (hRAD)R21CA273984 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI HUTCHESON, KATHERINE ARNOLD, SANCHEZ TERRONES, BENJAMIN · 2023 to 2024
$387k
NCI NIH HHS P01 CA285249NCI NIH HHS R01 CA257814NCI NIH HHS R01 CA258827NCI NIH HHS R01 CA271223NCI NIH HHS R21 CA273984NIDCR NIH HHS R01 DE028290
6 · The paper itself

Abstract

BACKGROUND & PURPOSE: Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient's health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models. MATERIALS &

methodsA multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥ 2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.

resultsDL models outperformed the conventional NTCP model in both the independent test set (AUC = 0.80-0.84 versus 0.76) and external test set (AUC = 0.73-0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.

conclusionDL NTCP models performed significantly better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.

Indexed as

Deep LearningDeglutition DisordersHead and Neck NeoplasmsRadiation InjuriesAgedFemaleHumansMaleMiddle AgedOrgans at RiskRadiotherapy DosageRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedDeep learningDysphagiaHead and neck cancerNormal Tissue Complication ProbabilityRadiation-induced toxicityRadiotherapyRisk prediction

Identifiers

PMID41033356
PMCPMC12520315

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.