为准确预测弥漫大B细胞淋巴瘤（DLBCL）患者的生存情况，利用深度学习技术从PET图像中提取健壮的预后影像标志物，并进行独立的外部验证。本回顾性研究纳入了来自三个独立医疗中心的684例DLBCL患者。利用VGG19和DenseNet121作为深度卷积神经网络结构，从PET图像中生成深度学习分数（DLS）。这些DLS被用来预测无进展生存期（PFS）和总生存期（OS）。此外，基于Cox比例风险模型的结果设计了多参数模型，并通过校准曲线、一致性指数（C-index）和决策曲线分析（DCA）在训练和验证队列中进行评估。 DLSPFS和DLSOS分别在训练和验证队列中与PFS和OS显著相关（P<0.05）。合并DLS的多参数模型显示出比竞争模型更优异的PFS（C-index: 0.866）和OS（C-index: 0.835）预测效果。在外部验证队列中，PFS和OS的C-index分别为0.760和0.770以及0.748和0.766，表明多参数模型的可靠有效性。校准曲线显示良好的一致性，决策曲线分析（DCA）证实多参数模型提供了更多的临床净效益。 DLS被鉴定为DLBCL患者生存的健壮预后影像标志物。此外，本研究开发的多参数模型在准确分层患者生存风险方面展示了很大的潜力。© 2023。作者（们）在Springer-Verlag GmbH Germany独家许可下，属于Springer Nature。

To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits.The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.