Optical Biopsy for Tissue Diagnostics of Squamous Cell Carcinoma in the Upper Aerodigestive Tract using Confocal Laser Endomicroscopy Imaging

点击翻译按钮获取中文摘要

Squamous cell carcinoma (SCC) accounts for over 90 percent of all cancer types in the oral cavity and pharynx, as well as for almost 100 percent of malignancies in the larynx. At present, the gold standard of diagnosis is an invasive biopsy of the tissue with subsequent histopathological assessment. One non-invasive method that has been successfully applied for visual inspection of suspicious mucosal lesions is Confocal Laser Endomicroscopy (CLE). With this in vivo imaging method, laser light is emitted and applied to tissue at a selected depth, the reflected fluorescence of light then being refocused for detection. Fluorescein is administered intravenous and distributed through the intercellular spaces without diffusing through the cell membranes, thus enabling outline visualization and structural analysis of cellular tissue. Due to its property of making cellular structures visible, CLE is said to provide ’real-time’ optical biopsies. CLE examination is highly dependent on examiners´experience and showed in previous publications variable diagnostic metrics. This motivated our group to introduce a new approach based on deep learning of automatic classification.Applying this new approach, we reached accuracies of 88.3% in a cross-validation scenario and , additionally, we demonstrated that it is possible to transfer classification knowledge acquired from epithelial CLE images of the oral cavity to epithelial CLE images of the vocal folds in the same data set, with even increased accuracies of 89.45%. These results suggest that CLE imaging data acquired from both of these anatomical locations can help to establish a general model that can distinguish normal tissue from malignancies in either of the two domains. As a result of the analysis of the state of the art and our own previous work, we intend to collect CLE image data during this applied funding period (24 months) in order to increase data quality and amount, which is suitable for the training of physicians as well as machine learning algorithms. This database will include benign/malignant mucosal lesions as well as physiological mucosa of the upper aerodigestive tract and will be released as open access data based on anonymized data. With this enriched data set, we intend to further improve on the state-of-the-art in automatic classification systems, by robustly detecting image artifacts and performing fine-grained classification of malign and benign structural changes at clinical level, both using deep learning approaches.