EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer.


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:
09 2023
Historique:
received: 28 03 2023
revised: 06 06 2023
accepted: 08 06 2023
medline: 21 8 2023
pubmed: 18 6 2023
entrez: 17 6 2023
Statut: ppublish

Résumé

To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.

Identifiants

pubmed: 37330055
pii: S0167-8140(23)00286-4
doi: 10.1016/j.radonc.2023.109748
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

109748

Subventions

Organisme : Austrian Science Fund FWF
ID : KLI 695
Pays : Austria

Informations de copyright

Copyright © 2023 The Authors. 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

Stefan Ecker (S)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria. Electronic address: stefan.ecker@meduniwien.ac.at.

Christian Kirisits (C)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Maximilian Schmid (M)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Johannes Knoth (J)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Gerd Heilemann (G)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Astrid De Leeuw (A)

University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands.

Alina Sturdza (A)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Kathrin Kirchheiner (K)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Nina Jensen (N)

Aarhus University Hospital, Department of Oncology, Aarhus, Denmark.

Remi Nout (R)

Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands.

Ina Jürgenliemk-Schulz (I)

University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands.

Richard Pötter (R)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

Sofia Spampinato (S)

Aarhus University Hospital, Department of Oncology, Aarhus, Denmark.

Kari Tanderup (K)

Aarhus University Hospital, Department of Oncology, Aarhus, Denmark.

Nicole Eder-Nesvacil (N)

Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.

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Classifications MeSH