ESTRO 2022 - Abstract Book

S614

Abstract book

ESTRO 2022

Teaching lecture: Leadership is a journey, not a position

SP-0682 Leadership is a journey, not a position

S. Turner 1

1 University of Sydney, Western Sydney Radiation Oncology Network, Sydney, Australia

Abstract Text The head of a major US broadcasting company in the early days of television famously said: “Leadership is action, not position”. This ethos, now a founding tenet of modern leadership theory speaks to the importance of leaders having a set of well-developed skills and behaviours in order to be effective. Occupying a named or official leadership position may provide authority but does not equate to effective leadership. The corollary is that effective leaders may not have any official role or title but possess core skills that allow them to bring others along in positive change processes. Yet knowing how to ‘act’ as a leader is not inherent. Fortunately, we now know these skills can be learned. For optimal healthcare systems, all health professionals need to be able to exhibit leadership at all levels of training and practice – in radiation oncology, the imperative to maximise leadership capability could not be more crucial. Like all professional learning, becoming a better leader is a life-long journey. This presentation will examine what leadership means for radiation and clinical oncology health professionals, how leadership skills and behaviours can be systematically developed through structured programs, the relevant educational research to date and what our future goals for radiation oncology leadership education might look like. The session will provide an opportunity for the audience to voice their opinions and contribute to discussion around this important issue.

Symposium: Automation in radiotherapy

SP-0683 For the clinician: Potential and ethical issues of fully automatic radiotherapy plan delivery and acceptance

D. Zips 1

1 Charite, Radiation Oncology, Berlin, Germany

Abstract Text Autonomous workflows using AI are feasible and will change radiation oncology. I will present our first experience and discuss the concept, opportunities, risks and ethical issues from the perspective of patients and radiation oncologists.

SP-0684 For the physicist: Current status of automised contouring, planning and QA

D. Verellen 1

1 Iridium Network, Faculty of Medicine and Health Sciences, Antwerp University, Radiation Oncology, Antwerp, Belgium

Abstract Text Radiation therapy is an extremely complex process that evolves and changes at a fast pace, and ensuring that our patients receive the intended treatment accurately remains a constant challenge to all disciplines involved. It is essential that the treatment is performed safely, but at the same time effective and efficient. Standardization and automation have shown to be powerful tools not only in increasing efficiency but also ensuring robust and high quality throughout these complex processes. In addition, it will be shown that standardization and individualization (or personalized treatment) are to be considered rather synergistic as opposed to contradicting concepts. As every new patient adds new information to the different steps in the radiotherapy workflow, generating a huge amount of structured data, the growing interest and impact of artificial intelligence (AI) has almost been inevitable. Machine learning (ML) applications are generally perceived as “black boxes”, which explains that their implementation is mainly accepted for automation of processes that can easily be inspected (eg delineation and treatment planning). Some examples will be provided in this presentation, illustrating the impact of automation in delineation and treatment planning on overall quality in the treatment process. Nevertheless, the increasing usage of AI models for automation creates awareness for the need of dedicated quality assurance (QA) procedures ensuring the safety of these AI processes (QA of AI). On the other hand, AI might become a powerful tool to enhance and optimize the QA procedures in the increasingly complex workflows, perhaps ultimately, generating a measureless framework to control and ensure the quality of the treatment delivery process (AI for QA). Whatever the application, it is obvious that the introduction of automation will require to rethink the role of the human operator from actually performing certain (routine) tasks, into training of models, monitoring the performance and safeguarding the quality of these processes.

SP-0685 For the RTT: Impact of automation on RTT practice

L. Uwamahoro-Irakiza

The Netherlands