ArticleCancer research2016
Label-Free Neurosurgical Pathology with Stimulated Raman Imaging.
Article in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed, 146 citations in OpenAlex.
- DeepLuAd: Semantic-guided virtual histopathology of lung adenocarcinoma via stimulated Raman scattering.Theranostics · 2026Article
- Label-free molecular profiling of cancer using Raman spectroscopy: from fundamentals to clinical applications.Frontiers in oncology · 2026Review
- Bond-Selective Imaging at the Frontier of Biomedicine.Chemical & biomedical imaging · 2025Article
- Construction of a whole-brain panorama for glioma vasculature reveals tumor heterogeneity and blood-brain barrier disruption.Science advances · 2025Article
- Multimodal Optical Imaging Platform for Studying Cellular Metabolism.Journal of visualized experiments : JoVE · 2025Article
- Probe-Based Fluorescence Spectroscopy for In Situ Brain Tumor Measurements During Resection and Needle Biopsies.Biomedicines · 2025Article
- Anisotropic hydrogel microelectrodes for intraspinal neural recordings in vivo.Nature communications · 2025Article
- Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging.Cancers · 2024Review
- Label-free optical imaging for brain cancer assessment.Trends in cancer · 2024Review
- Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level.ACS central science · 2024Review
- Noninvasive Nonlinear Optical Computational Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.Bioinformatics (Oxford, England) · 2023Article
- Accelerating Cancer Histopathology Workflows with Chemical Imaging and Machine Learning.Cancer research communications · 2023Article
- A deep learning-based stripe self-correction method for stitched microscopic images.Nature communications · 2023Article
- Stimulated Raman Scattering Imaging Sheds New Light on Lipid Droplet Biology.The journal of physical chemistry. B · 2023Article
- Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network.Cancer research · 2023Article
- Raman Spectroscopy as a Tool to Study the Pathophysiology of Brain Diseases.International journal of molecular sciences · 2023Review
- Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications.Biosensors · 2022Review
- Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation.Tomography (Ann Arbor, Mich.) · 2022Article
- Review
3 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
The goal of brain tumor surgery is to maximize tumor removal without injuring critical brain structures. Achieving this goal is challenging as it can be difficult to distinguish tumor from nontumor tissue. While standard histopathology provides information that could assist tumor delineation, it cannot be performed iteratively during surgery as freezing, sectioning, and staining of the tissue require too much time. Stimulated Raman scattering (SRS) microscopy is a powerful label-free chemical imaging technology that enables rapid mapping of lipids and proteins within a fresh specimen. This information can be rendered into pathology-like images. Although this approach has been used to assess the density of glioma cells in murine orthotopic xenografts models and human brain tumors, tissue heterogeneity in clinical brain tumors has not yet been fully evaluated with SRS imaging. Here we profile 41 specimens resected from 12 patients with a range of brain tumors. By evaluating large-scale stimulated Raman imaging data and correlating this data with current clinical gold standard of histopathology for 4,422 fields of view, we capture many essential diagnostic hallmarks for glioma classification. Notably, in fresh tumor samples, we observe additional features, not seen by conventional methods, including extensive lipid droplets within glioma cells, collagen deposition in gliosarcoma, and irregularity and disruption of myelinated fibers in areas infiltrated by oligodendroglioma cells. The data are freely available in a public resource to foster diagnostic training and to permit additional interrogation. Our work establishes the methodology and provides a significant collection of reference images for label-free neurosurgical pathology. Cancer Res; 76(12); 3451-62. ©2016 AACR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.