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

使用共聚焦激光内镜成像光学活检对上呼吸消化道鳞状细胞癌进行组织诊断

• 批准号: 439264659
• 负责人: Privatdozent Dr. Miguel Goncalves
• 金额: --
• 依托单位: Klinik und Poliklinik für Hals-, Nasen- und Ohrenkrankheiten, plastische und ästhetische Operationen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/439264659?language=en
• 关键词: Optical; Biopsy; Tissue; Diagnostics; Squamous

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.

鳞状细胞癌（SCC）占口腔和咽部所有癌症类型的90%以上，以及喉恶性肿瘤的近100%。目前，诊断的金标准是对组织进行侵入性活检，随后进行组织病理学评估。一种已经成功应用于可疑粘膜病变的视觉检查的非侵入性方法是共聚焦激光显微内镜检查（CLE）。 利用这种体内成像方法，激光被发射并施加到选定深度的组织，然后光的反射荧光被重新聚焦用于检测。黄绿素通过静脉内给药，并通过细胞间隙分布，而不扩散通过细胞膜，从而使细胞组织的轮廓可视化和结构分析成为可能。由于其使细胞结构可见的特性，CLE据说可以提供“实时”光学活检。CLE检查高度依赖于检查者的经验，并在以前的出版物中显示了可变的诊断指标。这促使我们的团队引入了一种基于深度学习的自动分类新方法。应用这种新方法，我们在交叉验证场景中达到了88.3%的准确率，此外，我们证明了可以将从口腔上皮CLE图像获得的分类知识转移到同一数据集中的声带上皮CLE图像，准确率甚至提高到89.45%。这些结果表明，从这两个解剖位置获取的CLE成像数据可以帮助建立一个通用模型，该模型可以区分两个域中的任一个中的正常组织与恶性肿瘤。根据对最新技术水平和我们之前工作的分析，我们打算在申请资助期间（24个月）收集CLE图像数据，以提高数据质量和数量，这适用于医生培训以及机器学习算法。该数据库将包括良性/恶性粘膜病变以及上呼吸消化道的生理性粘膜，并将基于匿名数据作为开放获取数据发布。有了这个丰富的数据集，我们打算进一步改进自动分类系统的最新技术，通过稳健地检测图像伪影并在临床水平上对恶性和良性结构变化进行细粒度分类，两者都使用深度学习方法。