Date & Time : 2 July 2026 | 16:00–17:00 CEST | Online
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Live journal club-style webinar with author presentations, expert discussant commentary, moderated discussion, and audience Q&A.
Overview
This first ESTRO Journal Club will discuss the phiRO paper “Harmonizing organ-at-risk structure names using open-source large language models.” The session will explore how open-source large language models may support standardization of radiotherapy structure nomenclature and consider broader implications for AI, interoperability, and clinical workflow integration.
Chairs
1 Barbara Knäusl, PhD, Editor-in-Chief, Physics and Imaging in Radiation Oncology, Medical University of Vienna, Austria
2 Kareem A. Wahid, MD, PhD, Associate Editor, Physics and Imaging in Radiation Oncology, The University of Texas MD Anderson Cancer Center, United States
Speakers
- Adrian Thummerer, PhD, University of Bern, Switzerland, (First author)
- Christopher Kurz, PhD, LMU University Hospital / Ludwig-Maximilians-Universität München, Germany, (Senior author)
- Charlotte Brouwer, PhD, University Medical Center Groningen, The Netherlands, (Discussant)
- Brian Anderson, PhD, University of California San Diego, United States, (Discussant)
Programme
16:00–16:05 | Welcome and introduction - Brief introduction to the ESTRO Journal Club format, the selected paper and related commentary, and participants. (moderator)
16:05–16:10 | Paper presentation - Introduction to the broader topic of the manuscript including some background on the initiation of the study. (senior author)
16:10–16:20 - Presentation of the selected phiRO paper, including study motivation, methods, key findings, and limitations. (first author)
16:20–16:35 | Discussant perspectives - Two expert discussants will each provide brief comments on the paper from complementary perspectives, including methodological considerations, clinical relevance, implementation challenges, and future directions.
16:35–16:58 | Moderated discussion and audience Q&A - Moderated discussion with the authors and discussants, incorporating audience questions and broader discussion of implications for AI-assisted standardization in radiotherapy.
16:58–17:00 | Closing remarks - Summary of key takeaways and closing comments.
Learning Objectives
What should participants understand or be able to do after attending?
Describe the rationale for harmonizing organ-at-risk structure names in radiotherapy.
- Summarise how open-source large language models can be applied to radiotherapy nomenclature standardization.
- Critically discuss the potential strengths, limitations, and implementation challenges of large language models -based approaches in clinical and research workflows.
- Recognise the broader relevance of structure-name standardization for AI development, data sharing, multi-institutional studies, and reproducibility in radiation oncology.
- Identify future opportunities for integrating AI-assisted standardization tools into radiotherapy practice and research infrastructure.
Resources