Foundation models for fast, label-free detection of glioma infiltration

Akhil Kondepudi, Melike Pekmezci, Xinhai Hou, Katie Scotford, Cheng Jiang, Akshay Rao, Edward S. Harake, Asadur Chowdury, Wajd Al-Holou, Lin Wang, Aditya Pandey, Pedro R. Lowenstein, Maria G. Castro, Lisa Irina Koerner, Thomas Roetzer-Pejrimovsky, Georg Widhalm, Sandra Camelo-Piragua, Misha Movahed-Ezazi, Daniel A. Orringer, Honglak Lee, Christian Freudiger, Mitchel Berger, Shawn Hervey-Jumper () and Todd Hollon ()
Additional contact information
Akhil Kondepudi: University of Michigan
Melike Pekmezci: San Francisco
Xinhai Hou: University of Michigan
Katie Scotford: San Francisco
Cheng Jiang: University of Michigan
Akshay Rao: University of Michigan
Edward S. Harake: University of Michigan
Asadur Chowdury: University of Michigan
Wajd Al-Holou: University of Michigan
Lin Wang: University of Michigan
Aditya Pandey: University of Michigan
Pedro R. Lowenstein: University of Michigan
Maria G. Castro: University of Michigan
Lisa Irina Koerner: Medical University Vienna
Thomas Roetzer-Pejrimovsky: Medical University of Vienna
Georg Widhalm: Medical University Vienna
Sandra Camelo-Piragua: University of Michigan
Misha Movahed-Ezazi: Rutgers University
Daniel A. Orringer: New York University
Honglak Lee: University of Michigan
Christian Freudiger: Inc.
Mitchel Berger: San Francisco
Shawn Hervey-Jumper: San Francisco
Todd Hollon: University of Michigan

Nature, 2025, vol. 637, issue 8045, 439-445

Abstract: Abstract A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection1–3. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival4–6. Here we present FastGlioma, a visual foundation model for fast (

Date: 2025
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DOI: 10.1038/s41586-024-08169-3

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