Confocal scanning microscopy provides rapid, detailed intraoperative histological assessment of brain neoplasms: Experience with 106 cases

Martirosyan, Nikolay L.; Georges, Joseph; Eschbacher, Jennifer; Belykh, Evgenii; Carotenuto, Alessandro; Spetzler, Robert F.; Nakaji, Peter; Preul, Mark C.

doi:10.1016/j.clineuro.2018.03.015

Cited by 22 publications

(33 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When combined with the appropriate fluorescent dye, CLE can be effective in visualizing characteristic tissue architecture and morphology for histopathologic analysis. Moreover, the CLE probe is similar in size to a standard neurosurgical suction device, permitting manual application in real-time intraoperative visualization of brain tumors and normal tissue at the cellular level [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

Belykh

Ngo

Farhadi

et al. 2020

JCM

Self Cite

View full text Add to dashboard Cite

This is the first study to assess confocal laser endomicroscopy (CLE) use within the transsphenoidal approach and show the feasibility of obtaining digital diagnostic biopsies of pituitary tumor tissue after intravenous fluorescein injection. We confirmed that the CLE probe reaches the tuberculum sellae through the transnasal transsphenoidal corridor in cadaveric heads. Next, we confirmed that CLE provides images with identifiable histological features of pituitary adenoma. Biopsies from nine patients who underwent pituitary adenoma surgery were imaged ex vivo at various times after fluorescein injection and were assessed by a blinded board-certified neuropathologist. With frozen sections used as the standard, pituitary adenoma was diagnosed as “definitively” for 13 and as “favoring” in 3 of 16 specimens. CLE digital biopsies were diagnostic for pituitary adenoma in 10 of 16 specimens. The reasons for nondiagnostic CLE images were biopsy acquisition <1 min or >10 min after fluorescein injection (n = 5) and blood artifacts (n = 1). In conclusion, fluorescein provided sufficient contrast for CLE at a dose of 2 mg/kg, optimally 1–10 min after injection. These results provide a basis for further in vivo studies using CLE in transsphenoidal surgery.

show abstract

Section: Discussionmentioning

confidence: 99%

Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

Belykh

Ngo

Farhadi

et al. 2020

JCM

Self Cite

View full text Add to dashboard Cite

show abstract

“…This association of laser power and gain with contrast and autofluorescence is shown schematically in Supplemental Figure 7. Compared with targeted fluorophores or reflectance imaging (45), imaging of FNa is always associated with higher levels of noise.…”

Section: Discussionmentioning

confidence: 99%

“…However, a zoom function might be advantageous for dyes such as AO or AF (Supplemental Figure 8). Unlike benchtop confocal scanning microscope systems, where zoom can be adjusted in real time (45, 46), handheld CLE systems are limited to a preset zoom because of limitations in miniature design and construction.…”

Section: Discussionmentioning

confidence: 99%

Progress in Confocal Laser Endomicroscopy for Neurosurgery and Technical Nuances for Brain Tumor Imaging With Fluorescein

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background: Previous studies showed that confocal laser endomicroscopy (CLE) images of brain tumors acquired by a first-generation (Gen1) CLE system using fluorescein sodium (FNa) contrast yielded a diagnostic accuracy similar to frozen surgical sections and histologic analysis. We investigated performance improvements of a second-generation (Gen2) CLE system designed specifically for neurosurgical use. Methods: Rodent glioma models were used for in vivo and rapid ex vivo CLE imaging. FNa and 5-aminolevulinic acid were used as contrast agents. Gen1 and Gen2 CLE images were compared to distinguish cytoarchitectural features of tumor mass and margin and surrounding and normal brain regions. We assessed imaging parameters (gain, laser power, brightness, scanning speed, imaging depth, and Z-stack [3D image acquisition]) and evaluated optimal values for better neurosurgical imaging performance with Gen2. Results: Efficacy of Gen1 and Gen2 was similar in identifying normal brain tissue, vasculature, and tumor cells in masses or at margins. Gen2 had smaller field of view, but higher image resolution, and sharper, clearer images. Other advantages of the Gen2 were auto-brightness correction, user interface, image metadata handling, and image transfer. CLE imaging with FNa allowed identification of nuclear and cytoplasmic contours in tumor cells. Injection of higher dosages of FNa (20 and 40 mg/kg vs. 0.1–8 mg/kg) resulted in better image clarity and structural identification. When used with 5-aminolevulinic acid, CLE was not able to detect individual glioma cells labeled with protoporphyrin IX, but overall fluorescence intensity was higher ( p < 0.01) than in the normal hemisphere. Gen2 Z-stack imaging allowed a unique 3D image volume presentation through the focal depth. Conclusion: Compared with Gen1, advantages of Gen2 CLE included a more responsive and intuitive user interface, collection of metadata with each image, automatic Z-stack imaging, sharper images, and a sterile sheath. Shortcomings of Gen2 were a slightly slower maximal imaging speed and smaller field of view. Optimal Gen2 imaging parameters to visualize brain tumor cytoarchitecture with FNa as a fluorescent contrast were defined to aid further neurosurgical clinical in vivo and rapid ex vivo use. Further validation of the Gen2 CLE for microscopic visualization and diagnosis of brain tumors is ongoing.

show abstract

“…Fluorescent dyes currently approved for use in vivo in the human brain include fluorescein sodium (FNa), indocyanine green, and 5-aminolevulinic acid (5-ALA) ( 9 , 15 , 16 ). Other fluorescent dyes, such as acridine orange, acriflavine (AF), and cresyl violet, can be used on human brain tissue ex vivo ( 12 , 17 ). In neurosurgical oncology, CLE has been used to rapidly obtain optical cellular and cytoarchitectural information about tumor tissue as the resection progresses and to interrogate the resection cavity ( 12 , 13 ).…”

Section: Introductionmentioning

confidence: 99%

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

et al. 2018

Self Cite

View full text Add to dashboard Cite

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. CLE images can be distorted by motion artifacts, fluorescence signals out of detector dynamic range, or may be obscured by red blood cells, and thus interpreted as nondiagnostic (ND). However, just a single CLE image with a detectable pathognomonic histological tissue signature can suffice for intraoperative diagnosis. Dealing with the abundance of images from CLE is not unlike sifting through a myriad of genes, proteins, or other structural or metabolic markers to find something of commonality or uniqueness in cancer that might indicate a potential treatment scheme or target. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/ND, glioma/nonglioma, tumor/injury/normal categories, and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow, and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.

show abstract

Confocal scanning microscopy provides rapid, detailed intraoperative histological assessment of brain neoplasms: Experience with 106 cases

Cited by 22 publications

References 36 publications

Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

Progress in Confocal Laser Endomicroscopy for Neurosurgery and Technical Nuances for Brain Tumor Imaging With Fluorescein

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Contact Info

Product

Resources

About