肿瘤浸润淋巴细胞（TIL）的存在与原发性黑色素瘤（PM）的良好预后相关联。最近，基于人工智能（AI）的数字病理学方法已被提出，用于血染和嗪儿溶解染色（H&E）图像（全切片图像，WSI）上TIL的标准化评估。在这里，我们应用了一种新的卷积神经网络（CNN）分析PM WSI，以自动评估TIL浸润并提取TIL评分。CNN在包括训练集（237 WSI，57,758个补丁）和独立测试集（70 WSI，29,533个补丁）在内的307个PM的回顾性队列中进行了训练和验证。通过对肿瘤补丁进行TIL存在与否的分类，我们识别出了一个基于AI的TIL密度指数（AI-TIL）。所提出的CNN在识别PM WSI中的TIL方面表现出高性能，在测试集上显示出100％的特异性和敏感性。我们证明了基于AI的TIL指数与传统TIL评估和临床预后相关。AI-TIL指数是一种独立的预后标记物，与良好的预后直接相关。完全自动和标准化的AI-TIL似乎优于传统方法在区分PM临床预后方面。进一步的研究需要开发一个易于使用的工具，以协助病理学家评估实体肿瘤的TIL在临床评估中的应用。版权所有 © 2023. Elsevier Inc. 发布。

The presence of tumor-infiltrating lymphocytes (TIL) has been associated with a favorable prognosis of primary melanoma (PM). The recent development of the artificial intelligence (AI) based approach in digital pathology has been proposed for the standardized assessment of TIL on hematoxylin and eosin (H&E)-stained images (whole slide images, WSI). Here, we have applied a new convolution neural network (CNN) analysis of PM WSI to automatically assess the infiltration of TILs and extract a TIL score. A CNN was trained and validated in a retrospective cohort of 307 PMs including a training set (237 WSI, 57,758 patches) and an independent testing set (70 WSI, 29,533 patches). After the classification of tumor patches by the presence or absence of TILs, we identified an AI-based TIL density index (AI-TIL). The proposed CNN demonstrated high performance in recognizing TILs in PM WSI, showing specificity and sensitivity of 100% on the testing set. We demonstrated that the AI-based TIL index correlated with conventional TIL evaluation and clinical outcome. The AI-TIL index was an independent prognostic marker directly associated with a favorable prognosis. A fully automated and standardized AI-TIL appears to be superior to conventional methods at differentiating PM clinical outcome. Further studies are required to develop an easy-to-use tool to assist pathologists to assess TILs in the clinical evaluation of solid tumors.Copyright © 2023. Published by Elsevier Inc.