基于口腔鳞状细胞癌体外荧光共聚焦显微镜数据的深度学习：一项可行性研究。
Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study.
作者信息
Shavlokhova Veronika, Sandhu Sameena, Flechtenmacher Christa, Koveshazi Istvan, Neumeier Florian, Padrón-Laso Víctor, Jonke Žan, Saravi Babak, Vollmer Michael, Vollmer Andreas, Hoffmann Jürgen, Engel Michael, Ristow Oliver, Freudlsperger Christian
机构信息
Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Department of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
出版信息
J Clin Med. 2021 Nov 16;10(22):5326. doi: 10.3390/jcm10225326.
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.
背景
离体荧光共聚焦显微镜（FCM）是一种用于快速自动化组织学检查的新型有效方法。相比之下，传统诊断方法主要依赖于组织病理学家的技能。在本研究中，我们首次通过离体FCM成像研究了卷积神经网络（CNN）对口腔鳞状细胞癌进行自动化分类的潜力。
材料与方法
收集20例患者的组织样本，切除后立即用离体共聚焦显微镜扫描，并进行组织病理学检查。对一种CNN架构（MobileNet）进行训练并测试其准确性。
结果
在我们的研究中，该模型在癌组织自动化分类中实现了0.47的灵敏度和0.96的特异性。
结论
在这项初步工作中，我们在有限数量的离体FCM图像上训练了一个CNN模型，并在癌组织自动化分类中取得了有前景的结果。有必要进行进一步的大样本研究，以便将该技术引入临床。
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