Diagnostic Computed Tomography Enabled Planning for Palliative Radiation Therapy: Removing the Need for a Planning Computed Tomography Scan.


Journal

Practical radiation oncology
ISSN: 1879-8519
Titre abrégé: Pract Radiat Oncol
Pays: United States
ID NLM: 101558279

Informations de publication

Date de publication:
Historique:
received: 03 06 2020
revised: 28 10 2020
accepted: 31 10 2020
pubmed: 14 11 2020
medline: 19 8 2021
entrez: 13 11 2020
Statut: ppublish

Résumé

This study aimed to investigate the feasibility of using diagnostic computed tomography (dCT) for palliative radiation planning, removing the need for a planning computed tomography (pCT) scan. A sequential 2-stage study was performed. Stage 1 was a retrospective analysis of 150 patients' dCTs and pCTs to review potential barriers to radiation planning, as well as assess the field of view (FOV), patient positioning, couch curvature, and Hounsfield unit (HU) variation, and its dosimetric impact. Stage 2 was a clinical implementation of dCT planning into the clinical care path. Eligible patients were simulated per the standard department protocol in the dCT position. Treatment was planned on the dCT and replicated on the pCT as a backup and comparator. The dCT plan was delivered with cone beam computed tomography (CT) image guidance. After treatment, the delivered plan was recalculated on the modified dCT to compare planned with delivered planning target volume (PTV) dose. Positron emission tomography-CT imaging was the most suited for diagnostic treatment planning. Metastases in the pelvis, abdomen, thoracic, and lumbar spines were the most reproducible. A curved, full-body vac-bag was designed to enable better replication of the posterior body curvature of dCT for treatment. There was minimal variation in mean HU from dCT to pCT scans. Dose difference due to HU variation in the thorax region due to the low-density tissue had the greatest variation. All patients in stage 2 (n = 30) were successfully treated using the dCT plan. Dosimetric evaluations were conducted comparing dCT and modified dCT plans, with the 95% dose coverage change in PTV between -2% to +2.5%. For palliative patients with bony and soft-tissue metastases, clinically acceptable plans can be produced using dCT. Diagnostic position can be replicated at treatment, eliminating the need for pCT with implications for streamlining and improving care for patients who require palliative radiation therapy.

Identifiants

pubmed: 33186781
pii: S1879-8500(20)30264-2
doi: 10.1016/j.prro.2020.10.010
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e146-e153

Informations de copyright

Copyright © 2020 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Auteurs

Shelley Wong (S)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

Stephanie Roderick (S)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

Alannah Kejda (A)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

John Atyeo (J)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

Kylie Grimberg (K)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

Brian Porter (B)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia.

Jeremy Booth (J)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia; University of Sydney, Sydney, New South Wales, Australia.

George Hruby (G)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.

Thomas Eade (T)

Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia. Electronic address: Thomas.Eade@health.nsw.gov.au.

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Classifications MeSH