为了比较未经治疗的结节病和恶性淋巴瘤（ML）的[18F]FDG PET/CT结果，并利用最大强度投影（MIP）[18F]FDG PET图像开发卷积神经网络（CNN）模型以区分这些疾病。我们回顾性收集了新诊断的结节病和ML患者在治疗前进行的[18F]FDG PET/CT的连续患者的数据。两位核医学放射科医师对图像进行了审查。使用MIP PET图像创建了CNN模型，并以k折交叉验证对其进行了评估。使用渐变加权类激活映射（Grad-CAM）可视化了兴趣点。总共包括56例结节病患者和62例ML患者。结节病患者纵隔淋巴结和肺部病变中有更明显的FDG积累，而ML患者颈部淋巴结中有更明显的积累（所有p < 0.001）。对于纵隔淋巴结，结节病患者在2、4、7和10级淋巴结中有显著的FDG积累（所有p < 0.01）。否则，ML患者的积累倾向于在1级淋巴结中（p = 0.08）。使用前矢状位和侧矢状位MIP图像的CNN模型平均准确率为0.890（95% CI: 0.804-0.977），敏感性为0.898（95% CI: 0.782-1.000），特异性为0.907（95% CI: 0.799-1.000），曲线下面积为0.963（95% CI: 0.899-1.000）。Grad-CAM显示模型聚焦于异常FDG积累的部位。基于FDG分布差异的CNN模型在区分结节病和ML方面表现出较高的性能。我们开发了一个使用[18F]FDG PET/CT的MIP图像区分结节病和恶性淋巴瘤的CNN模型。它取得了较高的性能，在诊断涉及多个器官和淋巴结的疾病方面可能是有用的。• 在治疗前比较整体[18F]FDG PET/CT结果时，结节病和恶性淋巴瘤患者的FDG分布存在差异。• 使用两个角度的最大强度投影PET图像训练的卷积神经网络（一种深度学习技术）表现出较高的性能。• 利用FDG分布差异的深度学习模型可能有助于区分那些病变特征在器官和淋巴结间广泛分布的疾病。© 2023年。作者独家授权给欧洲放射学协会。

To compare the [18F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [18F]FDG PET images.We retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [18F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k-fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM).A total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804-0.977), a sensitivity of 0.898 (95% CI: 0.782-1.000), a specificity of 0.907 (95% CI: 0.799-1.000), and an area under the curve of 0.963 (95% CI: 0.899-1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation.CNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML.We developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes.• There are differences in FDG distribution when comparing whole-body [18F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.© 2023. The Author(s), under exclusive licence to European Society of Radiology.