scholarly journals Neuropathological interpretation of stimulated Raman histology images of brain and spine tumors: part B

Author(s):  
Jakob Straehle ◽  
Daniel Erny ◽  
Nicolas Neidert ◽  
Dieter Henrik Heiland ◽  
Amir El Rahal ◽  
...  

Abstract Intraoperative histopathological examinations are routinely performed to provide neurosurgeons with information about the entity of tumor tissue. Here, we quantified the neuropathological interpretability of stimulated Raman histology (SRH) acquired using a Raman laser imaging system in a routine clinical setting without any specialized training or prior experience. Stimulated Raman scattering microscopy was performed on 117 samples of pathological tissue from 73 cases of brain and spine tumor surgeries. A board-certified neuropathologist — novice in the interpretation of SRH — assessed image quality by scoring subjective tumor infiltration and stated a diagnosis based on the SRH images. The diagnostic accuracy was determined by comparison to frozen hematoxylin–eosin (H&E)-stained sections and the ground truth defined as the definitive neuropathological diagnosis. The overall SRH imaging quality was rated high with the detection of tumor cells classified as inconclusive in only 4.2% of all images. The accuracy of neuropathological diagnosis based on SRH images was 87.7% and was non-inferior to the current standard of fast frozen H&E-stained sections (87.3 vs. 88.9%, p = 0.783). We found a substantial diagnostic correlation between SRH-based neuropathological diagnosis and H&E-stained frozen sections (κ = 0.8). The interpretability of intraoperative SRH imaging was demonstrated to be equivalent to the current standard method of H&E-stained frozen sections. Further research using this label-free innovative alternative vs. conventional staining is required to determine to which extent SRH-based intraoperative decision-making can be streamlined in order to facilitate the advancement of surgical neurooncology.

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

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.


2013 ◽  
Vol 125 (49) ◽  
pp. 13280-13284 ◽  
Author(s):  
Ping Wang ◽  
Junjie Li ◽  
Pu Wang ◽  
Chun-Rui Hu ◽  
Delong Zhang ◽  
...  

2010 ◽  
Vol 49 (32) ◽  
pp. 5476-5479 ◽  
Author(s):  
Brian G. Saar ◽  
Yining Zeng ◽  
Christian W. Freudiger ◽  
Yu-San Liu ◽  
Michael E. Himmel ◽  
...  

Optica ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 103 ◽  
Author(s):  
Chi Zhang ◽  
Kai-Chih Huang ◽  
Bartek Rajwa ◽  
Junjie Li ◽  
Shiqi Yang ◽  
...  

2013 ◽  
Vol 85 (10) ◽  
pp. 5055-5063 ◽  
Author(s):  
Jessica C. Mansfield ◽  
George R. Littlejohn ◽  
Mark P. Seymour ◽  
Rob J. Lind ◽  
Sarah Perfect ◽  
...  

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