Intelligent Histology for Tumor Neurosurgery

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Hou, Xinhai, Kondepudi, Akhil, Jiang, Cheng, Lyu, Yiwei, Harake, Samir, Chowdury, Asadur, Meißner, Anna-Katharina, Neuschmelting, Volker, Reinecke, David, Furtjes, Gina, Widhalm, Georg, Koerner, Lisa Irina, Straehle, Jakob, Neidert, Nicolas, Scheffler, Pierre, Beck, Juergen, Ivan, Michael, Shah, Ashish, Pandey, Aditya, Camelo-Piragua, Sandra, Heiland, Dieter Henrik, Schnell, Oliver, Freudiger, Chris, Young, Jacob, Pekmezci, Melike, Scotford, Katie, Hervey-Jumper, Shawn, Orringer, Daniel, Berger, Mitchel, Hollon, Todd
Formato:	Preprint
Publicado:	2025
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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author	Hou, Xinhai Kondepudi, Akhil Jiang, Cheng Lyu, Yiwei Harake, Samir Chowdury, Asadur Meißner, Anna-Katharina Neuschmelting, Volker Reinecke, David Furtjes, Gina Widhalm, Georg Koerner, Lisa Irina Straehle, Jakob Neidert, Nicolas Scheffler, Pierre Beck, Juergen Ivan, Michael Shah, Ashish Pandey, Aditya Camelo-Piragua, Sandra Heiland, Dieter Henrik Schnell, Oliver Freudiger, Chris Young, Jacob Pekmezci, Melike Scotford, Katie Hervey-Jumper, Shawn Orringer, Daniel Berger, Mitchel Hollon, Todd
author_facet	Hou, Xinhai Kondepudi, Akhil Jiang, Cheng Lyu, Yiwei Harake, Samir Chowdury, Asadur Meißner, Anna-Katharina Neuschmelting, Volker Reinecke, David Furtjes, Gina Widhalm, Georg Koerner, Lisa Irina Straehle, Jakob Neidert, Nicolas Scheffler, Pierre Beck, Juergen Ivan, Michael Shah, Ashish Pandey, Aditya Camelo-Piragua, Sandra Heiland, Dieter Henrik Schnell, Oliver Freudiger, Chris Young, Jacob Pekmezci, Melike Scotford, Katie Hervey-Jumper, Shawn Orringer, Daniel Berger, Mitchel Hollon, Todd
contents	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 present 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 periperal 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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_03037
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Intelligent Histology for Tumor Neurosurgery Hou, Xinhai Kondepudi, Akhil Jiang, Cheng Lyu, Yiwei Harake, Samir Chowdury, Asadur Meißner, Anna-Katharina Neuschmelting, Volker Reinecke, David Furtjes, Gina Widhalm, Georg Koerner, Lisa Irina Straehle, Jakob Neidert, Nicolas Scheffler, Pierre Beck, Juergen Ivan, Michael Shah, Ashish Pandey, Aditya Camelo-Piragua, Sandra Heiland, Dieter Henrik Schnell, Oliver Freudiger, Chris Young, Jacob Pekmezci, Melike Scotford, Katie Hervey-Jumper, Shawn Orringer, Daniel Berger, Mitchel Hollon, Todd Computer Vision and Pattern Recognition 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 present 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 periperal 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.
title	Intelligent Histology for Tumor Neurosurgery
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2507.03037

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