Personnalised Medicine in Radiation Oncology 

 

Chairs: 

• Maximilian Niyazi, Radiation Oncologist, University Hospital Tübingen, Germany

• Piet Ost, Radiation Oncologist, Iridium Network, GZA Ziekenhuizen, Belgium

 

Speakers: 

To be confirmed

 

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