ArticleActa neuropathologica communications2022
Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy.
Article in Acta neuropathologica communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
- AI-driven label-free Raman spectromics for intraoperative spinal tumor assessment.NPJ digital medicine · 2026Article
- Rapid intraoperative boundary diagnosis of somatotroph pituitary neuroendocrine tumors using real-time PCR.Acta neuropathologica communications · 2026Article
- Frozen agarose-assisted tissue preparation for stimulated Raman histology enables rapid intraoperative assessment of peripheral nerve integrity.Brain & spine · 2026Article
- Fast intraoperative detection of primary central nervous system lymphoma and differentiation from common central nervous system tumors using stimulated Raman histology and deep learning.Neuro-oncology · 2025Article
- Confocal Laser Endomicroscopy: Enhancing Intraoperative Decision Making in Neurosurgery.Diagnostics (Basel, Switzerland) · 2025Review
- Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging.Cancers · 2024Review
- Fast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.medRxiv : the preprint server for health sciences · 2024Article
- Review
- Virtual Staining of Nonfixed Tissue Histology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Review
- [Intraoperative stimulated Raman histology for personalized brain tumor surgery].Chirurgie (Heidelberg, Germany) · 2024Review
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Advances in Intraoperative Glioma Tissue Sampling and Infiltration Assessment.Brain sciences · 2023Review
- Technical report: surgical preparation of human brain tissue for clinical and basic research.Acta neurochirurgica · 2023Article
- Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence.Frontiers in oncology · 2023Article
- Neurooncology: 2023 update.Free neuropathology · 2023Article
- Advances, technological innovations, and future prospects in stereotactic brain biopsies.Neurosurgical review · 2022Review
- Intelligent histology for tumor neurosurgery.Neuro-oncology advancesReview
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Determining the presence of tumor in biopsies and the decision-making during resections is often dependent on intraoperative rapid frozen-section histopathology. Recently, stimulated Raman scattering microscopy has been introduced to rapidly generate digital hematoxylin-and-eosin-stained-like images (stimulated Raman histology) for intraoperative analysis. To enable intraoperative prediction of tumor presence, we aimed to develop a new deep residual convolutional neural network in an automated pipeline and tested its validity. In a monocentric prospective clinical study with 94 patients undergoing biopsy, brain or spinal tumor resection, Stimulated Raman histology images of intraoperative tissue samples were obtained using a fiber-laser-based stimulated Raman scattering microscope. A residual network was established and trained in ResNetV50 to predict three classes for each image: (1) tumor, (2) non-tumor, and (3) low-quality. The residual network was validated on images obtained in three small random areas within the tissue samples and were blindly independently reviewed by a neuropathologist as ground truth. 402 images derived from 132 tissue samples were analyzed representing the entire spectrum of neurooncological surgery. The automated workflow took in a mean of 240 s per case, and the residual network correctly classified tumor (305/326), non-tumorous tissue (49/67), and low-quality (6/9) images with an inter-rater agreement of 89.6% (κ = 0.671). An excellent internal consistency was found among the random areas with 90.2% (Cα = 0.942) accuracy. In conclusion, the novel stimulated Raman histology-based residual network can reliably detect the microscopic presence of tumor and differentiate from non-tumorous brain tissue in resection and biopsy samples within 4 min and may pave a promising way for an alternative rapid intraoperative histopathological decision-making tool.
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