Abstract：
Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed on CT scans or metabolic assessment on FDG PET/CT, have some disadvantages, including high inter- and intraobserver variability and lack of clear cut-off points. The aim of this paper was to present a novel approach to fully automated segmentation of thoracic lymphoma in pediatric patients. Manual segmentations of 30 CT scans from 30 different were prepared by the authors. nnU-Net, an open-source deep learning-based segmentation method, was used for the automatic segmentation. The highest Dice score achieved by the model was 0.81 (SD = 0.17) on the test set, which proves the potential feasibility of the method, albeit it must be underlined that studies on larger datasets and featuring external validation are required. The trained model, along with training and test data, is shared publicly to facilitate further research on the topic.
摘要：
截至2020年，淋巴瘤是第九大最常见的恶性肿瘤，也是发达国家最常见的血液恶性肿瘤。淋巴瘤的分期和监测有多种方法，但是所有目前可用的，通常基于CT扫描的二维测量或FDGPET/CT的代谢评估，有一些缺点，包括观察者之间和观察者内部的高变异性和缺乏明确的截止点。本文的目的是提出一种新颖的方法来全自动分割儿科患者的胸部淋巴瘤。作者准备了30个不同CT扫描的手动分割。nnU-Net,一种开源的基于深度学习的分割方法，用于自动分割。模型在测试集上获得的最高Dice分数为0.81（SD=0.17），这证明了该方法的潜在可行性，尽管必须强调的是，需要对更大的数据集进行研究并进行外部验证。训练好的模型,以及训练和测试数据，公开分享，以促进对该主题的进一步研究。
