Ex vivo confocal microscopy features of cutaneous squamous cell carcinoma

2018 ◽  
Vol 11 (4) ◽  
pp. e201700318 ◽  
Author(s):  
Daniela Hartmann ◽  
Sebastian Krammer ◽  
Mario R. Bachmann ◽  
Leonie Mathemeier ◽  
Thomas Ruzicka ◽  
...  
Author(s):  
Veronika Shavlokhova ◽  
Christa Flechtenmacher ◽  
Sameena Sandhu ◽  
Michael Vollmer ◽  
Jürgen Hoffmann ◽  
...  

2019 ◽  
Vol 20 (8) ◽  
pp. 2009 ◽  
Author(s):  
Matthew J. Bottomley ◽  
Jason Thomson ◽  
Catherine Harwood ◽  
Irene Leigh

Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer. In immunosuppressed populations it is a source of considerable morbidity and mortality due to its enhanced recurrence and metastatic potential. In common with many malignancies, leucocyte populations are both protective against cancer development and also play a role in ‘sculpting’ the nascent tumor, leading to loss of immunogenicity and tumor progression. UV radiation and chronic viral carriage may represent unique risk factors for cSCC development, and the immune system plays a key role in modulating the response to both. In this review, we discuss the lessons learned from animal and ex vivo human studies of the role of individual leucocyte subpopulations in the development of cutaneous SCC. We then discuss the insights into cSCC immunity gleaned from studies in humans, particularly in populations receiving pharmacological immunosuppression such as transplant recipients. Similar insights in other malignancies have led to exciting and novel immune therapies, which are beginning to emerge into the cSCC clinical arena.


2021 ◽  
Vol 10 (22) ◽  
pp. 5326
Author(s):  
Veronika Shavlokhova ◽  
Sameena Sandhu ◽  
Christa Flechtenmacher ◽  
Istvan Koveshazi ◽  
Florian Neumeier ◽  
...  

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.


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