Radio-opaque contrast agents for liver cancer targeting with KIM during radiation therapy (ROCK-RT): an observational feasibility study.


Journal

Radiation oncology (London, England)
ISSN: 1748-717X
Titre abrégé: Radiat Oncol
Pays: England
ID NLM: 101265111

Informations de publication

Date de publication:
08 Oct 2024
Historique:
received: 09 01 2024
accepted: 17 09 2024
medline: 9 10 2024
pubmed: 9 10 2024
entrez: 8 10 2024
Statut: epublish

Résumé

This observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular carcinoma. This study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are ≤ 2 mm and ≤ 3 mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial participants. Developing a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial markers. Registered to ClinicalTrials.gov (NCT05169177) 12th October 2021.

Sections du résumé

BACKGROUND BACKGROUND
This observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular carcinoma.
METHODS METHODS
This study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are ≤ 2 mm and ≤ 3 mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial participants.
DISCUSSION CONCLUSIONS
Developing a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial markers.
TRIAL REGISTRATION BACKGROUND
Registered to ClinicalTrials.gov (NCT05169177) 12th October 2021.

Identifiants

pubmed: 39380004
doi: 10.1186/s13014-024-02524-4
pii: 10.1186/s13014-024-02524-4
doi:

Substances chimiques

Contrast Media 0

Banques de données

ClinicalTrials.gov
['NCT05169177']

Types de publication

Journal Article Observational Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

139

Subventions

Organisme : Cancer Institute NSW
ID : 2019/TPG2165
Organisme : Cancer Institute NSW
ID : 2019/TPG2165
Organisme : Cancer Institute NSW
ID : 2019/TPG2165
Organisme : National Health and Medical Research Council
ID : APP1194004

Informations de copyright

© 2024. The Author(s).

Références

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Auteurs

Natalie Plant (N)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.

Adam Mylonas (A)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.

Chandrima Sengupta (C)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.

Doan Trang Nguyen (DT)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.

Shona Silvester (S)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.

David Pryor (D)

Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.

Peter Greer (P)

Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, NSW, Australia.

Yoo Young Dominique Lee (YYD)

Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.

Prabhakar Ramachandran (P)

Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.

Venkatakrishnan Seshadri (V)

Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.

Yuvnik Trada (Y)

Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, NSW, Australia.

Richard Khor (R)

Olivia Newton-John Cancer Wellness & Research Centre, Austin Health, Melbourne, VIC, Australia.

Tim Wang (T)

Department of Radiation Oncology, Crown Princess Mary Cancer Centre, Sydney, NSW, Australia.

Nicholas Hardcastle (N)

Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.

Paul Keall (P)

Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia. paul.keall@sydney.edu.au.

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Classifications MeSH