Hubbard-Perez Magdalena, Luchian Andreea, Milford Charles, Ressel Lorenzo
DiMoLab, Institute of Infection Veterinary and Ecological Sciences, Department of Veterinary Anatomy Physiology and Pathology, University of Liverpool, Liverpool, United Kingdom.
Front Vet Sci. 2024 Jan 12;10:1309877. doi: 10.3389/fvets.2023.1309877. eCollection 2023.
Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the application of AI in domestic species are scarce. This research aims to employ deep learning (DL) through convolutional neural networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes ( = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; = 5), lymphoblastic (LBL; = 5), and marginal zone lymphoma (MZL; = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and validate the ability of the network to classify a broader range of lymphomas.
由于数字病理学技术的进步，人工智能在对人类不同类型肿瘤进行分类的能力方面取得了显著增长。在这些肿瘤中，淋巴瘤在犬类中相当常见，尽管关于人工智能在家养动物中的应用研究很少。本研究旨在通过卷积神经网络（CNN）运用深度学习（DL）来区分正常淋巴结和世界卫生组织（WHO）定义的犬淋巴瘤的3种常见亚型。为了训练和验证CNN，使用了从全切片扫描（WSI）获得的1530张高分辨率显微图像，包括背景区域、增生性淋巴结（n = 4）以及三种不同淋巴瘤亚型的图像：弥漫性大B细胞淋巴瘤（DLBCL；n = 5）、淋巴细胞白血病（LBL；n = 5）和边缘区淋巴瘤（MZL；n = 3）。CNN能够从457个可能的测试集中正确识别456张图像，最高准确率达到99.34%。本研究结果证明了使用深度学习区分增生性淋巴结和淋巴瘤以及对WHO常见亚型进行分类的可行性。需要进一步研究来探讨这些发现的意义，并验证该网络对更广泛类型淋巴瘤进行分类的能力。
