Intelligent Histology for Tumor Neurosurgery

2025-07-02

AI Summary

Intraoperative histopathological analysis has long suffered from slow turnaround times, reliance on labeled staining protocols, and absence of real-time digital imaging capabilities. To address these limitations, this study introduces the first end-to-end intraoperative digital pathology workflow integrating label-free stimulated Raman histology (SRH) with deep learning—enabling second-scale, high-resolution imaging, tumor molecular subtyping, and precise infiltration boundary delineation. We innovatively deploy a multicenter federated learning framework coupled with multimodal data fusion to overcome data silos. The workflow is rigorously validated across diverse neurosurgical scenarios—including gliomas, skull base, spinal, pediatric, and peripheral nerve tumors—achieving classification and boundary detection accuracy comparable to gold-standard hematoxylin–eosin (H&E)-stained histopathology. This paradigm significantly enhances intraoperative decision-making efficiency and advances intelligent pathology toward real-time, standardized, and clinically deployable solutions.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

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.

Problem

• Replace slow traditional histology with rapid AI-enhanced digital imaging
• Enable real-time tumor analysis and molecular classification during surgery
• Improve neurosurgical outcomes through multimodal AI-driven tissue evaluation

Innovation

• AI integrates with stimulated Raman histology
• SRH enables rapid digital tumor imaging
• AI-driven analysis for histology and classification