Fluorescence <i>In Vivo</i> Endomicroscopy Part 2: Applications of High-Resolution, 3-Dimensional Confocal Laser Endomicroscopy

Rangrez, Mohammedayaz; Bussau, Lindsay; Ifrit, Kishwar; Preul, Mark C.; Delaney, Peter

doi:10.1017/s1551929521000729

Cited by 3 publications

(5 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…18 Alternative dyes are available for topical use including fluorescein sodium, acridine orange, cresyl violet, acetic acid and indocyanine green. 12,28 Intravenous fluorescein is the most commonly used agent in the colon, esophagus and stomach, 28 and would be perfectly suited datasets. 30 Furthermore, future application of machine learning algorithms for enhanced diagnostic imaging can provide standardisation of diagnostic features and facilitate interpretation, which would be particularly useful for the non-specialist clinician/pathologist operator.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Alternative dyes are available for topical use including fluorescein sodium, acridine orange, cresyl violet, acetic acid and indocyanine green 12,28 . Intravenous fluorescein is the most commonly used agent in the colon, esophagus and stomach, 28 and would be perfectly suited for head and neck resections and margin delineation. Importantly though, CLE has the ability to enhance the utility of precision molecular imaging through the coupling of fluorescent dyes with molecular targets of interest such as EGFR, VEGF, PARP coupled to fluorescein isothiocyanate, 29 Alexa Fluor 488, BODIPY FL, or Green Fluorescent Protein (GFP); all suitable for use with 488 nm blue excitation light.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic real‐time optical microscopy of oral mucosal lesions using confocal laser endomicroscopy

Farah

Janík

Woo

et al. 2023

J Oral Pathology Medicine

View full text Add to dashboard Cite

Objectives Confocal laser endomicroscopy (CLE) is a novel non‐invasive point‐of‐care optical biopsy technology that enables real‐time in vivo microscopic visualisation of cellular and tissue architecture. In this study, we assessed the diagnostic accuracy of a hand‐held fluorescence single‐fibre distal‐scanning CLE (fsdCLE) platform for diagnosing oral epithelial dysplasia (OED) and oral squamous cell carcinoma (OSCC). Materials and Methods Forty‐seven patients presenting with 63 distinct oral mucosal lesions were subjected to optical biopsy using a miniaturised fsdCLE system (ViewnVivo®, Optiscan Imaging Ltd) and topical exogenous acriflavine hydrochloride contrast agent before undergoing tissue biopsy and histopathological consensus review by four pathologists. CLE images were captured in vivo in real‐time during clinical examination and assessed on‐the‐fly for the presence of cellular and architectural features of OED/OSCC offering an instantaneous diagnosis. Predicted optical diagnoses were compared to definitive consensus tissue histopathology. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated for the presence/absence of dysplasia/malignancy on optical biopsy. Percentage agreement, Fleiss' kappa, and intraclass correlation coefficient (ICC) were calculated for each assessment stage during the consensus histopathology process. Results Diagnostic accuracy was extremely high at 88.9%. Other metrics were sensitivity 86.8%, specificity 92%, PPV 94.3% and NPV 82.1%. One hundred percent of carcinoma cases were detected accurately using CLE in the clinic. Conclusion fsdCLE is a highly accurate, easy‐to‐use, rapid and slide‐free point‐of‐care in vivo optical technology for diagnosing OED/OSCC and discriminating between dysplastic and non‐dysplastic pathology. It demonstrates near‐perfect agreement with traditional consensus histopathology without the need for physical tissue biopsy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic real‐time optical microscopy of oral mucosal lesions using confocal laser endomicroscopy

Farah

Janík

Woo

et al. 2023

J Oral Pathology Medicine

View full text Add to dashboard Cite

show abstract

“…Briefly, the InVivage® system is a hand-held scanned fibre confocal endomicroscope that acquires images using a single channel for illumination and detection of 488 nm excitation, with a lens NA of 0.3 (similar to a 10 × objective) and a field of view of 475 µm × 475 µm. The resolution capabilities are 0.55 µm lateral and 5.1 µm axial The system is used by placing the probe in direct contact with the tissue, after treatment with a dye, to acquire images at different depths along the Z axis ( 28 ). The physical image plane can be translated from the surface to 400 µm but imaging depth with topical application was typically from tissue surface to a depth of 100 µm.…”

Section: Methodsmentioning

confidence: 99%

Acquisition and annotation in high resolution in vivo digital biopsy by confocal microscopy for diagnosis in oral precancer and cancer

Yap

Tan

Ramani³

et al. 2023

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

IntroductionScanned fibre endomicroscopes are full point-scanning confocal microscopes with submicron lateral resolution with an optical slice thickness thin enough to isolate individual cell layers, allow active positioning of the optical slice in the z-axis and collection of megapixel images. Here we present descriptive findings and a brief atlas of an acquisition and annotation protocol high resolution in vivo capture of oral mucosal pathology including oral squamous cell carcinoma and dysplasia using a fluorescence scanned fibre endomicroscope with 3 topical fluorescent imaging agents: fluorescein, acriflavine and PARPi-FL.MethodsDigital biopsy was successfully performed via an acquisition protocol in seventy-one patients presenting for investigation of oral mucosal abnormalities using a miniaturized, handheld scanned fibre endoscope. Multiple imaging agents were utilized and multiple time points sampled. Fifty-nine patients had a matched histopathology correlating in location with imaging. The images were annotated back to macrographic location using a purpose-built software, MouthMap™.ResultsAcquisition and annotation of cellular level resolved images was demonstrated with all 3 topical agents. Descriptive observations between clinically or histologically normal oral mucosa showed regular intranuclear distance, a regular nuclear profile and fluorescent homogeneity. This was dependent on the intraoral location and type of epithelium being observed. Key features of malignancy were a loss of intranuclear distance, disordered nuclear clustering and irregular nuclear fluorescence intensity and size. Perinuclear fluorescent granules were seen in the absence of irregular nuclear features in lichenoid inflammation.DiscussionHigh resolution oral biopsy allows for painless and rapid capture of multiple mucosal sites, resulting in more data points to increase diagnostic precision. High resolution digital micrographs can be easily compared serially across multiple time points utilizing an annotation software. In the present study we have demonstrated realization of a high-resolution digital biopsy protocol of the oral mucosa for utility in the diagnosis of oral cancer and precancer..

show abstract

“…In recent years, there has been increased interest worldwide in exploring methods for the prevention, diagnosis, and improved visualization of CRC [ 2 , 3 , 4 , 5 ]. Most techniques [ 9 , 10 , 11 , 12 ] integrate confocal laser endomicroscopy (CLE) and narrow banding imaging into the tip of a flexible endoscope to provide high-resolution endomicroscopy for real-time virtual biopsies. For example, the Cellvizio probe-based CLE has been commercialized.…”

Section: Introductionmentioning

confidence: 99%

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10× magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis.

show abstract

Fluorescence In Vivo Endomicroscopy Part 2: Applications of High-Resolution, 3-Dimensional Confocal Laser Endomicroscopy

Cited by 3 publications

References 74 publications

Dynamic real‐time optical microscopy of oral mucosal lesions using confocal laser endomicroscopy

Dynamic real‐time optical microscopy of oral mucosal lesions using confocal laser endomicroscopy

Acquisition and annotation in high resolution in vivo digital biopsy by confocal microscopy for diagnosis in oral precancer and cancer

Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning

Contact Info

Product

Resources

About