PDF(Pubmed)
Abstract：
Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.
摘要：
基于人工智能（AI）的技术越来越多地被探索作为一种新兴的辅助技术，用于提高组织病理学诊断的准确性和可重复性。肾细胞癌（RCC）是一种恶性肿瘤，占全球癌症死亡的2％。鉴于肾癌是一种异质性疾病，准确的组织病理学分类对于将侵袭性亚型与惰性亚型和良性拟态亚型分开至关重要。使用AI进行RCC分类以区分RCC的2种和3种亚型，早期有希望的结果。然而,目前尚不清楚基于AI的模型是如何为多个亚型的RCC设计的，良性模仿者会表演，这是一个更接近病理学真实实践的场景。使用252个整片图像（WSI）（透明细胞RCC：56，乳头状RCC：81，发色细胞RCC：51，透明细胞乳头状RCC：39和，后肾腺瘤：6）。298,071个补丁用于开发基于AI的图像分类器。298,071个补丁（350×350像素）用于开发基于AI的图像分类器。将该模型应用于二级数据集，并证明47/55（85％）WSI被正确分类。除了区分透明细胞RCC和透明细胞乳头状RCC外，该计算模型显示出出色的结果。需要使用多机构大型数据集和前瞻性研究进行进一步验证，以确定转化为临床实践的潜力。
