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<b><i>"Brain Tumor Growth Modeling for Radiotherapy Planning"</i></b></div>
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<b>Jonas Weidner</b></div>
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Technical University of Munich </div>
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Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend. </div>
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NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants only. </div>
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<b>https://fsu.zoom.us/j/94273595552 </b></div>
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Meeting # <b>942 7359 5552 </b></div>
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🎦 Colloquium recordings will be made available here,</div>
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<b>Wednesday, Nov 12, 2025, Schedule: </b></div>
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☕ Nespresso & Teatime - 417 DSL Commons </div>
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* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
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<b>🕟 Colloquium - 499 DSL Seminar Room </b></div>
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<b>* 3:30 to 4:30 PM Eastern Time (US and Canada) </b></div>
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<b>Abstract:</b></div>
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Accurate modeling of brain tumor growth holds great promise for improving radiotherapy planning by capturing microscopic infiltration that remains invisible on standard imaging. In this talk, I will present a series of advances toward patient-specific biophysical
and data-driven models of glioma growth. Our work integrates mechanistic tumor modeling with machine learning to enable accurate, efficient, and individualized predictions of tumor spread. By combining reaction-diffusion formulations with structural information
from diffusion tensor imaging, we capture how white matter architecture shapes glioma invasion. A learnable inference framework then bridges these biophysical models with data-driven parameter estimation, enabling fast and robust reconstruction of patient-specific
tumor cell distributions from MRI. Together, these developments move toward clinically applicable tumor growth modeling for personalized radiotherapy planning.</div>
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<img id="image_0" style="max-width: 794px;" size="124221" src="cid:e9d41a1e-deb5-48a7-aa81-e35056955ad3"></div>
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Additional colloquium details can be found here, </div>
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