Background: Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. Material and Methods: Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. Results: The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. Conclusion: In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.
Background Real‐time microscopic imaging of freshly excised oral squamous cell carcinomas (OSCCs) would be potentially supportive in rapid recognition of oral malignancy and an optimal and time‐saving management of patients' surgical treatment. Objectives The aim of this study was to examine oral squamous cell cancer tissue in regards to the commonly known and well‐described histomorphologic criteria for the diagnosis of OSCC in ex vivo confocal fluorescent microscopy and to analyze its correlation with grade of differentiation and level of invasiveness. Methods Ex vivo confocal laser scanning microscopy (CLSM) images of 38 OSCCs were evaluated immediately after excision for presence or absence of various cytological and architectural features based on the histopathological background. Next, these features were compared to the grade of differentiation as elaborated via gold standard histologic examination. Results Of 38 invasive OSCCs, 14 were well differentiated, while three moderately and 19 were poorly differentiated. The presence of the commonly known cytologic and histopathologic criteria for the diagnosis of oral squamous cell carcinoma such as the destruction of the basal cell membrane, cellular and nuclear pleomorphism, anisocytosis, intraepithelial keratinization, nuclear hyperchromasia, atypical mitotic figures as well as the presence of necrosis, and mixed inflammation could be observed in ex vivo fluorescence confocal microscopy (FCM). In ex vivo fluorescence confocal microscopy pictures, cellular pleomorphism and anisocytosis were observed more often in poorly differentiated OSCCs. Intraepithelial keratinization was associated with well differentiated and moderately differentiated OSCCs. Conclusion The results demonstrate the high potential of ex vivo fluorescence confocal microscopy in fresh tissue for rapid real‐time diagnosis of OSCC.
Real‐time microscopic imaging of freshly excised tissue enables a rapid bedside‐pathology. A possible application of interest is the detection of oral squamous cell carcinomas (OSCCs). The aim of this study was to analyze the sensitivity and specificity of ex vivo fluorescence confocal microscopy (FCM) for OSCCs and to compare confocal images visually and qualitatively with gold standard histopathology. Two hundred eighty ex vivo FCM images were prospectively collected and evaluated immediately after excision. Every confocal image was blindly assessed for the presence or absence of malignancy by two clinicians and one pathologist. The results were compared with conventional histopathology with hematoxylin and eosin staining. OSCCs were detected with a very high sensitivity of 0.991, specificity of 0.9527, positive predictive value of 0.9322 and negative predictive value of 0.9938. The results demonstrate the potential of ex vivo FCM in fresh tissue for rapid real‐time surgical pathology.
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