An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.

Deep Learning Head Head and Neck Neoplasms / diagnostic imaging Humans Magnetic Resonance Spectroscopy Radiotherapy Planning, Computer-Assisted
Auto contouring Head and Neck MR Linac MR guided radiotherapy Machine Learning

Journal

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192

Informations de publication

Date de publication:
05 2021
Historique:
received: 15 10 2020
revised: 02 02 2021
accepted: 15 02 2021
pubmed: 27 2 2021
medline: 21 5 2021
entrez: 26 2 2021
Statut: ppublish

Résumé

Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy. Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (model Model A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.

Identifiants

DOI: 10.1016/j.radonc.2021.02.018 PMID: 33636229
pubmed: 33636229
pii: S0167-8140(21)06081-3
doi: 10.1016/j.radonc.2021.02.018
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

112-117

Subventions

Organisme : Cancer Research UK
ID : A21993
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C147/A25254
Pays : United Kingdom

Informations de copyright

Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

C Hague (C)

Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Christina.hague@nhs.net.

A McPartlin (A)

Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Andrew.mcpartlin@nhs.net.

L W Lee (LW)

Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: lipwai.lee@nhs.net.

C Hughes (C)

Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: christopher.hughes@christie.nhs.uk.

D Mullan (D)

Department of Radiology, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Damian.mullan@nhs.net.

W Beasley (W)

Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: w.beasley@nhs.net.

A Green (A)

Division of Cancer Sciences, Faculty of Biology, Medicine and Heath, University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Andrew.green-2@manchester.ac.uk.

G Price (G)

Division of Cancer Sciences, Faculty of Biology, Medicine and Heath, University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Gareth.price@manchester.ac.uk.

P Whitehurst (P)

Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Philip.whitehurst1@nhs.net.

N Slevin (N)

Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK.

M van Herk (M)

Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Heath, University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: marcel.vanherk@manchester.ac.uk.

C West (C)

Division of Cancer Sciences, Faculty of Biology, Medicine and Heath, University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Catharine.west@manchester.ac.uk.

R Chuter (R)

Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Heath, University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK. Electronic address: Robert.chuter@nhs.net.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Apprentissage profond › An evaluation of MR based deep learning...
questionsmedicales.fr › Régions du corps › Tête › An evaluation of MR based deep learning...
questionsmedicales.fr › Tumeurs › Tumeurs par siège › Tumeurs de la tête et du cou › An evaluation of MR based deep learning...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › An evaluation of MR based deep learning...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Analyse spectrale › Spectroscopie par résonance magnétique › An evaluation of MR based deep learning...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Prise de décision assistée par ordinateur › Thérapie assistée par ordinateur › Radiothérapie assistée par ordinateur › Planification de radiothérapie assistée par ordinateur › An evaluation of MR based deep learning...