原文：Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve [Formula: see text], accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text] and F1-score [Formula: see text], while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome.All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.© 2023. The Author(s). 原发性中枢神经系统淋巴瘤（PCNSL）是一种罕见且具有侵袭性的非淋巴结型非何杰金淋巴瘤。提前预测总体生存率（OS）对于帮助临床决策具有至关重要的作用。尽管放射影像学机器学习（ML）在PCNSL方面表现出良好的性能，但要求事先从磁共振影像中进行大量手动特征提取的工作。而深度学习（DL）则克服了这种限制。在本文中，我们定制了3D ResNet来预测患有PCNSL病人的OS。为了克服数据稀疏的限制，我们引入了数据增强和转移学习，并使用分层k折交叉验证进行结果评估。为了解释我们模型的结果，应用了梯度加权的类激活映射。与基于ML的临床数据和放射影像学数据的模型相比，我们在增强后的T1加权（T1Gd）图像的面积曲线下，准确度、精度、召回率和F1得分方面获得了最佳表现（标准误差），进一步验证了我们模型的稳定性。此外，我们观察到PCNSL是一种全脑疾病，在OS少于1年的情况下，更难区分肿瘤边界和脑的正常部分，这与临床结果一致。所有这些发现表明，T1Gd可以提高预测患有PCNSL病人的预后。据我们所知，这是第一次使用DL来解释PCNSL患者OS分类模型的模式。未来的研究将涉及收集更多的PCNSL患者数据，或者对罕见病不同患者人群进行追溯性研究，以进一步推广我们模型的临床作用。 © 2023.作者。

Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.In this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.We obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve [Formula: see text], accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text] and F1-score [Formula: see text], while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome.All these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.© 2023. The Author(s).