Wang Xi, Li Bin-Bin
Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China.
Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China.
Front Genet. 2021 Feb 10;12:624820. doi: 10.3389/fgene.2021.624820. eCollection 2021.
Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.
头颈部肿瘤是第六大常见肿瘤。多组学整合了临床、病理、放射学和生物学数据的多个维度，具有肿瘤诊断和分析的潜力。深度学习（DL）作为人工智能（AI）的一种类型，被应用于医学图像分析。在深度学习技术中，卷积神经网络（CNN）用于图像分割、检测和分类以及计算机辅助诊断。在此，我们综述了使用CNN和其他深度学习神经网络对头颈部肿瘤进行多组学图像分析的情况。我们还评估了其在肿瘤早期检测、分类、预后/转移预测以及报告签发方面的应用。最后，我们强调了这些技术面临的挑战和潜力。
