scholarly journals Stimulated Raman scattering microscopy for rapid brain tumor histology

2017 ◽  
Vol 10 (05) ◽  
pp. 1730010 ◽  
Author(s):  
Yifan Yang ◽  
Lingchao Chen ◽  
Minbiao Ji

Rapid histology of brain tissues with sufficient diagnostic information has the great potential to aid neurosurgeons during operations. Stimulated Raman Scattering (SRS) microscopy is an emerging label-free imaging technique, with the intrinsic chemical resolutions to delineate brain tumors from normal tissues without the need of time-consuming tissue processing. Growing number of studies have shown SRS as a “virtual histology” tool for rapid diagnosis of various types of brain tumors. In this review, we focus on the basic principles and current developments of SRS microscopy, as well as its applications for brain tumor imaging.

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kseniya S. Shin ◽  
Andrew T. Francis ◽  
Andrew H. Hill ◽  
Mint Laohajaratsang ◽  
Patrick J. Cimino ◽  
...  

AbstractIntraoperative consultations, used to guide tumor resection, can present histopathological findings that are challenging to interpret due to artefacts from tissue cryosectioning and conventional staining. Stimulated Raman histology (SRH), a label-free imaging technique for unprocessed biospecimens, has demonstrated promise in a limited subset of tumors. Here, we target unexplored skull base tumors using a fast simultaneous two-channel stimulated Raman scattering (SRS) imaging technique and a new pseudo-hematoxylin and eosin (H&E) recoloring methodology. To quantitatively evaluate the efficacy of our approach, we use modularized assessment of diagnostic accuracy beyond cancer/non-cancer determination and neuropathologist confidence for SRH images contrasted to H&E-stained frozen and formalin-fixed paraffin-embedded (FFPE) tissue sections. Our results reveal that SRH is effective for establishing a diagnosis using fresh tissue in most cases with 87% accuracy relative to H&E-stained FFPE sections. Further analysis of discrepant case interpretation suggests that pseudo-H&E recoloring underutilizes the rich chemical information offered by SRS imaging, and an improved diagnosis can be achieved if full SRS information is used. In summary, our findings show that pseudo-H&E recolored SRS images in combination with lipid and protein chemical information can maximize the use of SRS during intraoperative pathologic consultation with implications for tissue preservation and augmented diagnostic utility.


2013 ◽  
Vol 5 (201) ◽  
pp. 201ra119-201ra119 ◽  
Author(s):  
M. Ji ◽  
D. A. Orringer ◽  
C. W. Freudiger ◽  
S. Ramkissoon ◽  
X. Liu ◽  
...  

2019 ◽  
Vol 63 (5) ◽  
pp. 2028-2034 ◽  
Author(s):  
Kristel Sepp ◽  
Martin Lee ◽  
Marie T. J. Bluntzer ◽  
G. Vignir Helgason ◽  
Alison N. Hulme ◽  
...  

2017 ◽  
Vol 19 (suppl_4) ◽  
pp. iv51-iv51
Author(s):  
Todd Hollon ◽  
Mia Garrard ◽  
Jamaal Tarpeh ◽  
Balaji Pandian ◽  
Yashar Niknafs ◽  
...  

2019 ◽  
Vol 116 (32) ◽  
pp. 15842-15848 ◽  
Author(s):  
Yuta Suzuki ◽  
Koya Kobayashi ◽  
Yoshifumi Wakisaka ◽  
Dinghuan Deng ◽  
Shunji Tanaka ◽  
...  

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.


2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi193-vi193 ◽  
Author(s):  
Daniel A. Orringer ◽  
Balaji Pandian ◽  
Todd C. Hollon ◽  
Yashar S. Niknafs ◽  
Julianne Boyle ◽  
...  

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