随着研究黑色素瘤转移机制和新治疗方法的努力不断增加，研究人员需要准确、高通量的方法来评估特定干预措施对肿瘤负荷的影响。我们证明，从整个幻灯片图像中自动量化肿瘤内容是评估体内实验的一个令人信服的解决方案。为了增加临床前研究数据收集的流出，我们组装了一个带有注释的大型数据集，并训练了一个深度神经网络，用于定量分析小鼠模型组织病理学切片上的黑色素瘤肿瘤内容。在评估其分割这些图像的性能后，该工具获得了与在实验环境中测量转移的正交方法（生物发光）一致的结果。这种基于人工智能的算法通过名为 MetFinder 的网络界面免费提供给学术实验室，有望成为对准确、定量评估转移负担感兴趣的黑色素瘤研究人员和病理学家的资产。© 2024 John Wiley

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.