Intelligent histology for tumor neurosurgery.
The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standard-of-care intraoperative pathology workflow is based on light microscopy and H&E histology, which is slow, resource-intensive, and lacks real-time digital imaging capabilities. Here, we describe an emerging and innovative method for intraoperative histologic analysis, called Intelligent Histology, that integrates artificial intelligence (AI) with stimulated Raman histology (SRH). SRH is a rapid, label-free, digital imaging method for real-time microscopic tumor tissue analysis. SRH generates high-resolution digital images of surgical specimens within seconds, enabling AI-driven tumor histologic analysis, molecular classification, and tumor infiltration detection. We review the scientific background, clinical translation, and future applications of intelligent histology in tumor neurosurgery. We focus on the major scientific and clinical studies that have demonstrated the transformative potential of intelligent histology across multiple neurosurgical specialties, including neurosurgical oncology, skull base, spine oncology, pediatric tumors, and peripheral nerve tumors. Future directions include the development of AI foundation models through multi-institutional datasets, incorporating clinical and radiologic data for multimodal learning, and predicting patient outcomes. Intelligent histology represents a transformative intraoperative workflow that can reinvent real-time tumor analysis for 21st century neurosurgery.
Authors
- Akhil Kondepudi (ORCID: https://orcid.org/0000-0002-2643-7580)
- Jacob S. Young (ORCID: https://orcid.org/0000-0002-5499-4325)
- Mitchel Stuart Berger (ORCID: https://orcid.org/0000-0003-1983-4892)
- Pierre Scheffler (ORCID: https://orcid.org/0000-0002-6596-456X)
- Katie Scotford
- David Reinecke (ORCID: https://orcid.org/0000-0002-3298-9517)
- Samir Harake
- Gina Fürtjes (ORCID: https://orcid.org/0000-0003-3727-9984)
- Volker Neuschmelting (ORCID: https://orcid.org/0000-0001-7527-6990)
- Nicolas Neidert (ORCID: https://orcid.org/0000-0003-0299-1138)
- Xinhai Hou
- Shawn L. Hervey‐Jumper (ORCID: https://orcid.org/0000-0003-4699-260X)
- Oliver Schnell (ORCID: https://orcid.org/0000-0002-1284-1427)
- Cheng Jiang (ORCID: https://orcid.org/0000-0003-1759-4960)
- Chris Freudiger
- Todd Charles Hollon (ORCID: https://orcid.org/0000-0001-5987-6531)
- Jürgen Beck (ORCID: https://orcid.org/0000-0002-7687-6098)
- Aditya S. Pandey (ORCID: https://orcid.org/0000-0003-0789-4273)
- Daniel A. Orringer (ORCID: https://orcid.org/0000-0001-6427-8376)
- Jakob Straehle (ORCID: https://orcid.org/0000-0003-3063-8972)
- Georg Widhalm (ORCID: https://orcid.org/0000-0001-6014-0273)
- Dieter Henrik Heiland (ORCID: https://orcid.org/0000-0002-9258-3033)
- Melike Pekmezci (ORCID: https://orcid.org/0000-0003-2548-8359)
- Asadur Chowdury (ORCID: https://orcid.org/0000-0002-5653-9721)
- Ashish S. Shah (ORCID: https://orcid.org/0000-0002-1821-9110)
- Yiwei Lyu (ORCID: https://orcid.org/0000-0002-3882-4246)
- Lisa Koerner (ORCID: https://orcid.org/0000-0002-9227-461X)
- Anna-Katharina Meißner (ORCID: https://orcid.org/0000-0003-4150-7265)
- Michael E. Ivan
- Sandra Camelo‐Piragua
Institutions
- University of Miami (US)
- University of California, San Francisco (US)
- University of Michigan (US)
- University Medical Center Freiburg (DE)
- NYU Langone Health (US)
- Universitätsklinikum Erlangen (DE)
- Invenio Imaging (United States) (US)
- University Hospital Cologne (DE)
- Medical University of Vienna (AT)
Publication Details
- Journal
- PubMed
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1093/noajnl/vdag065
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00