Personalised Medicine in Radiation Oncology

Personalised Medicine in Radiation Oncology

Chairs:

Maximilian Niyazi, Radiation Oncologist, University Hospital Tübingen, Germany
Piet Ost, Radiation Oncologist, Iridium Network, GZA Ziekenhuizen, Belgium

Speakers:

Claudio Fiorino, Medical Physicist, Università Vita-Salute San Raffaele, Italy
Robert Jeraj, Medical Physicist, University of Wisconsin School of Medicine and Public Health, USA
Calvin Chao, Vice President, Medical Science, Artera AI
Chris Talbot, Radiobiologist, University of Leicester, United Kingdom
Michael Orth, Radiobiologist, University Hospital Tübingen, Germany
Thomas Bortfeld, Medical Physicist, Harvard Medical School, USA

Target Audience (with clear clinical focus):

Radiation oncologists, medical physicists, radiation therapists, oncology nurses, and other healthcare professionals involved in radiation oncology care.

Description:

Personalized radiation oncology is advancing rapidly through the integration of genomic, imaging, and clinical biomarkers to guide and adapt treatment strategies in real time. A central example of this transformation is Optimal Stopping in Radiotherapy (OSRT), a data-driven framework that enables adaptive treatment based on longitudinal assessments of patient-specific radiosensitivity. OSRT incorporates evolving information from tumor and normal tissue biology, imaging, and circulating biomarkers to move beyond static dose prescriptions and toward dynamically optimized therapies.

At the genomic level, indices such as the Radiosensitivity Index (RSI) and the Genomic-Adjusted Radiation Dose (GARD) have been developed to refine radiation dosing based on tumor biology. However, despite robust theoretical and preclinical foundations, these tools have yet to demonstrate consistent clinical utility – highlighting the need for critical appraisal and rigorous validation before clinical implementation. Large-scale initiatives such as the REQUITE consortium have underscored the complexity of translating predictive biomarkers into routine practice, while experiences from biomarker-guided toxicity prediction (e.g., PROSTOX in prostate cancer) provide valuable insights into design and validation challenges.

Beyond genomics, advanced imaging and adaptive delivery techniques – such as MRI- and PET-guided planning, online/offline adaptation, FLASH radiotherapy, and targeted radiopharmaceuticals – offer promising avenues for personalization. However, these technologies also raise fundamental questions about which patients benefit most and how best to prioritize innovation in a resource-constrained environment.

Artificial intelligence (AI) and machine learning are increasingly pivotal in this landscape, especially in harnessing large-scale treatment databases and integrating patient-reported outcome measures (PROMs). These approaches offer the potential to develop learning healthcare systems capable of improving treatment decisions based on individual patient histories.

The workshop will address these themes through a series of expert-led sessions, with a strong focus on learning from past translational challenges. Despite the substantial amount of available data, many promising models and biomarkers have not reached clinical translation. We will explore why, and how better-designed trials and registries – informed by lessons from initiatives like OSRT and REQUITE – can overcome these barriers. Finally, the role of physician entrepreneurs will be examined, including whether economic incentives and commercial urgency can accelerate innovation more effectively than traditional academic pathways.

This comprehensive discussion aims to critically assess current approaches and inspire forward-thinking strategies for implementing truly personalized radiation therapy.

Objectives:

What are ideal use cases to personalize treatments?
Which trials or registries are needed to integrate biomarkers in future?
Link existing ideas and organ experts/youngsters & building a network throughout ESTRO
Establish collaboration Physics/Stats/Database experts Explore emerging technologies and methods in precision radiation therapy
Discuss implementation strategies for patient-centered approaches in clinical practice

Disease sites of interest

Brain
H&N
GU
Breast
Lung
GI, e.g. rectum
More possible

Outcomes:

Enhanced collaboration between physics/clinicians
Opportunities for funding and support to advance future trials

Workshop Structure

Day 1 – Thursday 12 March

Time	Session
09:00–09:15	Welcome & Introduction of the workshop (all participants together)
09:15–10:15	Opening lecture Innovative trial design: Statistics (e.g. Bayesian designs), how to build (retro)spective registries, cloud solutions, digitalisation, AI & machine learning (all participants together)
10:15–10:45	Coffee break
10:45–12:45	Theme 1: Genomics and (imaging) Biomarkers in Radiation Response Speakers: Michael Orth & Robert Jeraj Genomic Signatures in Radiation Sensitivity and critical appraisal – Radiosensitivity indices (RSIs) – Genomic-adjusted radiation dose (GARD) approach – Biological underpinnings of individual radiosensitivity
12:45–13:45	Lunch
13:45–15:45	Theme 2: Normal Tissue Biomarkers Speakers: Chris Talbot, Piet Ost & Calvin Chao Predictive vs. prognostic biomarkers Multi-omics approaches (genomics, proteomics, radiomics) Lessons learned from the REQUITE consortium (C. Talbot): large European consortium trying to discover biomarkers for toxicity in prostate, lung and breast cancer https://pubmed.ncbi.nlm.nih.gov/31146072/ Commercial attempts – physician entrepreneurship (C. Chao): Artera.ai: pathology MMAI model that predicts who needs systemic therapy in prostate cancer in combination with radiotherapy. Example: PROSTOX in prostate cancer predicts urinary toxicity
15:45–16:15	Coffee break
16:15–17:15	Theme 2: Normal Tissue Biomarkers Speakers: Chris Talbot, Piet Ost & Calvin Chao Predictive vs. prognostic biomarkers Rest of Theme 2
17:15–18:15	Progress reports of the five breakout groups (all participants together)
18:15–19:30	Networking event

Day 2 – Friday 13 March

Time	Session
08:00–10:00	Theme 4: OSRT & Treatment/Dose Adaptation Speakers: Thomas Bortfeld, Claudio Fiorino & Maximilian Niyaz Optimal Stopping in Radiotherapy (OSRT) – trial design – OSRT as a decision framework – Mid-treatment biomarker monitoring – Sequential treatment adaptation models
10:00–10:30	Coffee break
10:30–12:00	Wrap-up and Discussion All participants together / 10 min per topic Key Takeaways and Implementation Strategies Future Research Directions Feedback and Evaluation
12:00–13:00	Lunch
13:00–14:00	Plenary lecture Science communication: e.g. social media workshop, how to podcast, how to produce video content, …. (all participants together)
14:00–15:00	Wrap up: reports, highlights and planned next steps of the five breakout groups (all participants together)
15:00–15:15	Closing remarks