Intelligent Intraoperative Histology in Neuro-oncological Surgery: Stimulated Raman Imaging, Foundation Models, and Real-time Molecular Classification
Year: 2026
Type: article
Abstract: Background. Intraoperative neuropathological diagnosis influences extent of resection, biopsy adequacy, tissue allocation, and therapeutic strategy, yet frozen sections and smear preparations are limited by processing time, artifacts, sampling error, and dependence on subspecialty expertise. Stimulated Raman histology combined with artificial intelligence provides rapid, label-free visualization of fresh tissue and may support tumor classification, infiltration mapping, and molecular prediction. Materials and methods. A structured narrative review integrated prospective diagnostic studies, multicenter cohorts, technical investigations, artificial-intelligence validation studies, rapid sequencing workflows, and consensus standards available through July 2026. Evidence was organized by optical principles, tissue acquisition, diagnostic accuracy, molecular inference, margin assessment, uncertainty calibration, workflow integration, safety, equity, and regulatory translation. A multicenter comparative study is proposed for adults undergoing resection or stereotactic biopsy of primary or metastatic central nervous system tumors. Results. Stimulated Raman histology with deep learning has achieved near-real-time classification of brain tumors, differentiation of lymphoma from glioblastoma and metastasis, molecular screening of diffuse gliomas, and quantification of microscopic infiltration. Foundation-model systems can analyze fresh specimens within seconds, whereas nanopore sequencing and ultra-rapid molecular assays may provide integrated diagnoses during surgery. The proposed primary endpoint is patient-level diagnostic concordance with final integrated neuropathological diagnosis combined with a relevant change in surgical management. Secondary endpoints include turnaround time, margin sensitivity, nondiagnostic sampling, calibration, subgroup performance, operative duration, neurological morbidity, tissue preservation, and cost-effectiveness. Conclusion. Intelligent intraoperative histology may transform neurosurgical pathology from episodic consultation into continuous decision support. Prospective external validation, explicit uncertainty, human oversight, and standardized reporting remain essential.
Source: MEDICAL RESEARCH JOURNAL
Author: Mukhammadov Nuriddin Askarovich
Institution: Samarkand State Medical Institute
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7220239486