ESTRO 2025 - Abstract Book

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Invited Speaker

ESTRO 2025

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Speaker Abstracts RTT competency framework for online IGART Winnie Li Radiation Therapy, Princess Margaret, Toronto, Canada

Abstract:

While time and resource demanding, online daily adaptive radiation therapy will become increasing common. As adaptive radiation therapy requires a greater level of autonomy, responsibility and accountability in practice, whether defined as advancing practice or advancing practice roles, adaptive therapy will impact the credentialing requirements for Radiation Therapists. Standardizing adaptive radiation therapy credentialing requirements for Radiation Therapists will ensure they have the skills, knowledge, and judgment to ensure safe delivery of these complex treatments.

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Speaker Abstracts Radiomics features Vincent W. S. Leung Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

Abstract:

Radiomics refers to the extraction of quantitative features from medical images, which are then used as imaging biomarkers to support clinical decision-making. These biomarkers aim to improve the accuracy of diagnosis, prognosis, and outcome prediction, which are critical components of personalized medicine. This presentation will introduce radiomics as a transformative approach in cancer care, emphasizing its potential to enhance personalized treatment strategies and improve patient outcomes after radiotherapy. As a radiation therapist, I will provide an accessible explanation of radiomics, focusing on its application in radiotherapy and its ability to predict treatment response and side effects. Radiomics bridges technical and clinical domains, essentially building correlation models between imaging data and clinical outcomes. Successful radiomics research requires a deep understanding of clinical needs, meaningful imaging data use, and dose and contour data integration. Radiation therapists are uniquely positioned to lead in this field due to our daily involvement in treatment planning, imaging, and patient care. Our hands-on experience with imaging data, dose delivery, and contouring gives us the clinical insight and technical expertise necessary to drive meaningful radiomics research. I will highlight two key projects demonstrating the value of radiation therapists-led radiomics research. The first project, "Integrating computed tomography (CT)-based Radiomic Model with Clinical Features Improves Long-Term Prognostication in High-Risk Prostate Cancer," showed that combining pre-treatment planning CT-based radiomic features with clinical attributes significantly improved the prediction of 5-year progression-free survival (PFS) in high-risk prostate cancer patients treated with prostate-only radiotherapy (PORT). The radiomic-clinical (RC) combined model outperformed models using radiomic or clinical features alone, achieving an area-under-the-curve (AUC) of 0.797 in the validation cohort. The second project, "Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Receiving Whole Pelvic Radiotherapy" explored the use of planning CT-based radiomics to predict 6-year PFS in high-risk prostate cancer patients undergoing whole pelvic