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Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Tuesday
May 10
09:15 - 10:30
Auditorium 11
ESTRO-ESR: Imaging biomarkers for personalised radiotherapy
Regina Beets-Tan, The Netherlands;
Uulke van der Heide, The Netherlands
Imaging biomarkers from CT, MRI and PET offer the promise of personalizing radiation oncology. In this joint symposium between the European Society of Radiology (ESR) and ESTRO, the work that is required to bring this promise to reality, will be addressed from a multi-disciplinary perspective. The physicist will address the technical challenges and standardization efforts, whereas the radiation oncologist will discuss the clinical potential of imaging biomarkers. The RTT will describe her role in integrating imaging in the radiotherapy workflow. Finally, the imaging expert will discuss how AI can be harnessed to develop prediction models based on imaging biomarkers.
Joint Symposium
Interdisciplinary
10:09 - 10:27
The imaging expert: The use of (imaging) biomarkers in AI prediction models of outcome
Ángel Alberich Bayarri, Spain
SP-0972

Abstract

The imaging expert: The use of (imaging) biomarkers in AI prediction models of outcome
Authors:

Ángel Alberich Bayarri1

1Quibim , Quibim, Valencia, Spain

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Abstract Text

Tumor heterogeneity and habitats are not mostly taken into account in current radiotherapy (RT) planning procedures. The extraction of quantitative imaging biomarkers allow for the voxelwise characterisation of lesions, ensuring spatial coherence across different imaging series by the use of optimized registration algorithms. Further development in the field of dose painting and formats for the integration of parametric maps (i.e. parametric DICOM) with RT planning software needs to be explored. Beyond planning, the assessment of RT treatment response as well as early detection of recurrence are key applications of the spatial biology capabilities that bring imaging biomarkers. In this presentation I will review the process for the extraction of accurate imaging biomarkers, accounting for sources of variability, and how to use them for RT planning as well as treatment response evaluation and prediction of recurrence.