S3016
Invited Speaker
ESTRO 2026
dose assessment, improving data consistency and interpretability. The integration of AI-driven tools into clinical trial workflows further enhances efficiency and quality control. Additionally, the development of the Operational Ontology for Oncology (O3), aligned with OMOP, CDISC, and HL7-FHIR standards, facilitates the aggregation of RTQA data from electronic health records, bridging clinical trial data with real-world evidence. Looking ahead, this standardized and increasingly automated RTQA framework is well-positioned for global adoption. Continued expansion to support emerging technologies—such as adaptive radiotherapy, FLASH, proton therapy, and radiomics— will be essential to maintain trial integrity and advance precision oncology. 5360 Multiplatform RTQA for oART trials: What are the risks and how do we ensure equity? Sarah Osman Radiotherapy Physics, University College London Hospitals NHS Foundation Trust, London, United Kingdom. National Radiotherapy Trials Quality Assurance (RTTQA) Group, University College London Hospitals NHS Foundation Trust, London, United Kingdom Online adaptive radiotherapy (oART) workflows are inherently high-risk. Even among centres equipped to participate in clinical trials, considerable variation in platforms, resources, and local processes means that current quality assurance (QA) frameworks are inequitable by design, and this goes to the heart of trial validity. Failure mode and effects analysis (FMEA) studies from multiple institutions consistently identify similar vulnerabilities, with failures driven primarily by human factors, time pressure, and process complexity. In a multiplatform, multicentre trial context, these risks are amplified by differences in system capabilities, staff training, and local workflows, making standardised yet site-sensitive QA design both challenging and critical. Drawing on the published literature and our own multicentre FMEA work, we map the highest-risk steps in oART workflows and examine what QA approaches can realistically address them. We consider both technical and human factors, and reflect on the challenge of balancing rigour with practicality across diverse centres, and on the importance of working collaboratively with institutions and vendors to build QA programmes that are robust, scalable, and sustainable.
presentation aims to explore these early-stage challenges. A key focus will be the Dunning-Kruger effect, which describes the mismatch between perceived and actual competence. In the context of starting a PhD, this effect may manifest as initial overconfidence followed by a rapid emergence of self-doubt as the complexity of research becomes apparent. This transition is closely linked to other cognitive biases, such as confirmation bias, which can shape how we interpret information. In parallel, the presentation will address impostor syndrome, a common experience among early-stage researchers characterised by persistent feelings of inadequacy despite clear evidence of competence. For RTTs transitioning into academia, this phenomenon may be amplified by the shift from clinical expertise to a novice researcher role. These concepts will be illustrated using insights from my PhD research on upright positioning for radiotherapy, highlighting concrete examples where cognitive biases and self-perception influence decision-making, learning processes, and interactions within multidisciplinary teams. Finally, the presentation will explore practical strategies to navigate these challenges, including developing reflective practices, seeking mentorship, and engaging in peer support networks. Recognising and understanding cognitive biases can empower RTTs to reframe their experiences, enhance resilience, and optimise their learning trajectory. 5358 A standardised reporting framework and the role of automation in RTQA for clinical trials Huaizhi Geng Radiation Oncology, University of Pennsylvania, Philadelphia, USA Radiotherapy quality assurance (RTQA) plays a critical role in determining clinical outcomes in oncology trials, as evidenced by landmark studies such as ProCLAIM, RTOG 0522, and RTOG 0617, where protocol deviations in target coverage and organ-at- risk (OAR) constraints were associated with poorer survival. These findings highlight the necessity of a standardized and rigorous RTQA reporting framework. In response, CIRO has developed a comprehensive infrastructure incorporating disease-specific templates, TG-263 nomenclature, and harmonized OAR definitions across more than 100 trials, enabling consistent and interoperable reporting across national and international research groups. Automation is emerging as a key enabler of scalable RTQA, with studies demonstrating that auto- segmentation reduces variability in contouring and
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