Stimulated Raman histology

Moskalik, Anzhela D.; Dastagirzada, Yosef; Orringer, Daniel

doi:10.1016/b978-0-323-85158-9.00024-5

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…With remarkably boosted Raman signals, simulated Raman scattering (SRS) microscopy is a desirable method of label-free and high speed molecular imaging at the single cell level 39,40 . In recent years, SRS microscopy with Raman tags 41 has been widely used in the study of cancer metabolism 42 and diagnosis 43 . For instance, Ji et al for the first time employed two-color SRS microscopy to achieve virtual H&E staining, that is stimulated Raman histology (SRH) 44,45 , and later on Hollon et al demonstrated deep learning-based SRH could realize intraoperative brain tumor diagnosis 46 .…”

Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-assisted Stimulated Raman Cytology

Chen

et al. 2023

Preprint

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Peritoneal metastasis (PM) is the most common form of distant metastasis and one of the leading causes of death in gastric cancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here we established the stimulated Raman cytology (SRC), a chemical microscopy-based intelligent cytology. By taking advantages of stimulated Raman scattering in label-free, high-speed, and high-resolution chemical imaging, we firstly imaged 53951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative), at the Raman bands corresponding to DNA, protein, and lipid, respectively. Then, we revealed 12 single cell features of morphology and composition that were significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, we developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix was crucial to identify the significant marker cell cluster, the divergence of which was finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, our SRC method assisted by machine learning classifiers could reach 81.5% sensitivity, 84.9% specificity, and the area under receiver operating characteristic curve of 0.85, within 20 minutes for each patient. Such remarkable improvement in detection accuracy is largely owing to incorporation of the single-cell composition features in SRC. Together, our SRC method shows great potential for accurate and rapid detection of PM from GC.

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Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-assisted Stimulated Raman Cytology

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[ 40,41 ] In recent years, SRS microscopy with Raman tags [ 42 ] has been widely used in the study of cancer metabolism [ 43 ] and diagnosis. [ 44 ] For instance, Ji et al. for the first time employed two‐color SRS microscopy to achieve virtual H&E staining, that is stimulated Raman histology (SRH), [ 45,46 ] and later on Hollon et al.…”

Section: Introductionmentioning

confidence: 99%

“…[40,41] In recent years, SRS microscopy with Raman tags [42] has been widely used in the study of cancer metabolism [43] and diagnosis. [44] For instance, Ji et al for the first time employed two-color SRS microscopy to achieve virtual H&E staining, that is stimulated Raman histology (SRH), [45,46] and later on Hollon et al demonstrated deep learning-based SRH could realize intraoperative brain tumor diagnosis. [47] The SRH method has been applied in diagnosis of multiple human cancers.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI‐Assisted Stimulated Raman Molecular Cytology

Chen

et al. 2023

Advanced Science

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Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

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Stimulated Raman histology

Cited by 2 publications

References 20 publications

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-assisted Stimulated Raman Cytology

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-assisted Stimulated Raman Cytology

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI‐Assisted Stimulated Raman Molecular Cytology

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