Intraoperative assessment of skull base tumors using stimulated Raman scattering microscopy

Abstract

Intraoperative 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.

Raman Imaging: An Impending Approach Towards Cancer Diagnosis

“…Fast simultaneous two‐channel SRS imaging technique was integrated with a pseudo H&E recoloring methodology, for the diagnosis of skull base tumors with 87% accuracy relative to conventional H&E‐staining. (Figure 4A) [66] …”

Recent advances in the use of stimulated Raman scattering in histopathology

“…30,31 With the increased use of digital pathology, machine learning (ML) approaches to classifying histopathological samples are being investigated as reviewed by Komura et al 32 The motivation for developing automated processes for interpreting slides is driven by a shortage of specialist pathologists and the potential to decrease the time to determine a preliminary diagnosis as discussed above. 7,33,34 The large data sets generated from SRH can lend themselves to ML approaches to rapidly classify tissue. Orringer et al, have developed ML approaches to enable automated diagnosis of CNS tumours from their SRH imagesas reviewed by Khalsa et al 35 Their approach has evolved from using vector-based multilayer perceptron (MLP) which are trained using specialised knowledge of CNS tumours to manually define important features (nuclear morphology, cell density, vascularity, etc.)…”

“…Shin et al, highlighted some of the limitations of using just SRH for the detection of complex pathologies. 34 Assessing a broad range of skull base tumours, where cytoarchitecture can be lost during freezing and sectioning, pseudo-H&E coloured SRH images achieved a relative accuracy of 87% compared to permanent sections. The high accuracy was mainly driven by a large number of easy to diagnose meningiomas.…”

Review of Stimulated Raman Scattering Microscopy Techniques and Applications in the Biosciences

“…SRS microscopy has also demonstrated great potential for brain tumor detection. [106][107][108] In recent studies, SRS was used to differentiate human glioblastoma multiforme (GBM) tumors from non-neoplastic in mouse models. [109] A classifier regarding the protein/lipid ratio, axonal density, and cellularity was proposed to detect tumor infiltration with a sensitivity of 97.5% and specificity of 98.5%, which showed great potential in fast and accurate intraoperative diagnosis.…”