Simultaneous implementation of unrelated tumour sites on the MR Linac: A review of the commissioning process from a radiographer perspective and lessons learned.

MR Linac MRI-guided adaptive radiotherapy MRL MRgART Radiotherapy

Journal

Journal of medical imaging and radiation sciences
ISSN: 1876-7982
Titre abrégé: J Med Imaging Radiat Sci
Pays: United States
ID NLM: 101469694

Informations de publication

Date de publication:
16 Aug 2024
Historique:
received: 08 01 2024
revised: 29 05 2024
accepted: 10 07 2024
medline: 18 8 2024
pubmed: 18 8 2024
entrez: 17 8 2024
Statut: aheadofprint

Résumé

This work reports on a systematic approach to select MRI sequences, quantify inter-observer image registration variation and determine patient positioning for the clinical implementation of MR-guided adaptive radiotherapy (MRgRT) in patients with oropharyngeal (H&N) and lung cancer. A total of 30 participants (N=10 H&N and N=10 lung cancer patients and N=10 healthy participants) were scanned on the Elekta Unity Magnetic Resonance Linear Accelerator (MRL). Participant experience questionnaires were used to determine the most appropriate positioning device for lung treatments and tolerability of H&N immobilization devices within the confined MR Linac environment. Visual guided assessments (VGAs) completed by three observers (one oncologist and two radiographers) were used to determine the most suitable tissue weighting (using vendor-provided 3D T1w and T2w sequences) for online image registration. Offline MRI to CT and MRI to MRI rigid registrations were undertaken by nine radiographers using bony and soft tissue matching. Single-factor ANOVA and paired t-tests were utilized to determine the interobserver variation. Based on oncologist and patient feedback, lung cancer patients would be treated in a vac-bag with their arms by their sides, while H&N cancer patients would be immobilized using a 5-point fixation device and 5-point personalized thermoplastic shell. There was no clear preference for T1w or T2w images in the H&N cohort. However, observers preferred T2w sequences for tumour and organ at risk (OAR) visualization in the lung images. When a bony match was conducted, single-factor ANOVA tests showed no statistically significant differences between all H&N image registration types (p=0.09). For the soft-tissue registrations, T1w-CT and T1w-T1w registrations showed a statistically significant (p=0.01) reduction in inter-observer variability over T2w-CT registrations. Paired t-tests showed no statistically significant differences for bony or soft tissue matches using T1w or T2w sequences to the planning CT in the lung cohorts (p=0.63 and p=0.52, respectively). We describe the systematic approach to the selection of strategies for imaging, immobilization, and online image registration we used for H&N and lung cancer treatments on the MRL. This has facilitated the selection of the most appropriate adaptive MRgRT strategies for treating these sites at our institution.

Identifiants

pubmed: 39153404
pii: S1939-8654(24)00459-4
doi: 10.1016/j.jmir.2024.101728
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

101728

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

A Clough (A)

The Christie NHSFT, Manchester, United Kingdom.

E Pitt (E)

The Christie NHSFT, Manchester, United Kingdom.

C Nelder (C)

The Christie NHSFT, Manchester, United Kingdom.

R Benson (R)

The Christie NHSFT, Manchester, United Kingdom.

L McDaid (L)

The Christie NHSFT, Manchester, United Kingdom.

L Whiteside (L)

The Christie NHSFT, Manchester, United Kingdom.

L Davies (L)

The Christie NHSFT, Manchester, United Kingdom.

J Bridge (J)

The Christie NHSFT, Manchester, United Kingdom.

L Freear (L)

The Christie NHSFT, Manchester, United Kingdom.

R Chuter (R)

The Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.

J Berresford (J)

The Christie NHSFT, Manchester, United Kingdom.

A McPartlin (A)

The Christie NHSFT, Manchester, United Kingdom.

C Crockett (C)

The Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.

D Cobben (D)

Clatterbridge Cancer Centre, Department of Clinical Oncology; Department of Health Data Science, Institute of Population Health, University of Liverpool.

A Salem (A)

The Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Faculty of Medicine, Hashemite University, Zarqa, Jordan.

C Faivre-Finn (C)

The Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.

R Huddart (R)

The Royal Marsden NHSFT, London, United Kingdom; The Institute for Cancer Research.

C L Eccles (CL)

The Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom. Electronic address: cynthia.eccles1@nhs.net.

Classifications MeSH