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

Stefan Ecker*, Christian Kirisits, Maximilian Schmid, Johannes Knoth, Gerd Heilemann, Astrid De Leeuw, Alina Sturdza, Kathrin Kirchheiner, Nina Jensen, Remi Nout, Ina Jürgenliemk-Schulz, Richard Pötter, Sofia Spampinato, Kari Tanderup, Nicole Eder-Nesvacil

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number109748
Number of pages9
JournalRadiotherapy and Oncology
Publication statusE-pub ahead of print - 15 Jun 2023

Bibliographical note

Funding Information:
This project was supported by the Austrian Science Fund, Austria (FWF, project number KLI695-B33).

Publisher Copyright:
© 2023 The Authors


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