Lucas Zimmer
(1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany)
Glioblastoma remains one of the greatest challenges in oncology, with near-universal recurrence largely driven by diffuse tumor infiltration beyond radiologically visible tumor margins, yet current radiotherapy planning relies on uniform geometric expansions that ignore patient-specific tumor biology and anatomy. Computational growth models and machine learning approaches have the potential to estimate these invisible tumor extensions and guide personalized radiotherapy planning, but their clinical translation has been limited by a lack of standardized benchmarking datasets and evaluation frameworks.
In this talk, I present current approaches to tumor growth modeling and recurrence prediction, including a novel U-Net-based model, and compare their performance against the current standard of care for radiotherapy planning. To this end, I introduce PREDICT-GBM, an end-to-end platform and curated dataset for evaluating computational models of glioblastoma growth and recurrence prediction. The results demonstrate that both biophysical and deep-learning approaches significantly outperform standard-of-care protocols in predicting future recurrence. Finally, I discuss what these comparisons reveal about the strengths and limitations of biophysical and data-driven approaches for guiding personalized radiotherapy, and outline the next steps toward clinical integration.
Lucas Zimmer
(1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany)
Jonas Weidner
(1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany 2Munich Center for Machine Learning (MCML))
Michal Balcerak
(3Department of Quantitative Biomedicine, University of Zurich, Switzerland)
Florian Kofler
(3Department of Quantitative Biomedicine, University of Zurich, Switzerland 4Helmholtz AI, Helmholtz Zentrum München, Germany)
Mara Krupa
(1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany)
Ivan Ezhov
(5AI in Healthcare and Medicine, Technical University of Munich, Munich, Germany)
Santiago Cepeda
(6Department of Neurosurgery, Río Hortega University Hospital, Valladolid, Spain 7Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain)
Ray Zhang
(8Department of Mathematical Sciences, Worcester Polytechnic Institute, USA)
John Lowengrub
(9Department of Mathematics, University of California, Irvine, USA 10Department of Biomedical Engineering, University of California, Irvine, USA)
Bjoern Menze
(3Department of Quantitative Biomedicine, University of Zurich, Switzerland)
Benedikt Wiestler
(1AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany 2Munich Center for Machine Learning (MCML))
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