目的:原发性中枢神经系统淋巴瘤(PCNSL)是一种罕见的,侵袭性形式的结外非霍奇金淋巴瘤。提前预测总生存期(OS)至关重要,因为它有可能帮助临床决策。尽管基于影像组学的机器学习(ML)在PCNSL中表现出了有希望的性能,它需要事先从磁共振图像中进行大量的人工特征提取。深度学习(DL)克服了这一限制。
方法:在本文中,我们定制了3DResNet来预测PCNSL患者的OS。为了克服数据稀疏性的限制,我们引入了数据增强和迁移学习,我们使用r分层k折交叉验证来评估结果。为了解释我们模型的结果,应用梯度加权类激活映射。
结果:我们在对比后T1加权(T1Gd)曲线下面积上获得最佳性能(标准误差)[公式:见正文],精度[公式:见文本],精度[公式:见文本],召回[公式:见文本]和F1得分[公式:见文本],与基于ML的临床数据和影像组学数据模型相比,分别,进一步证实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小于1年的情况下,很难区分肿瘤边界和大脑的正常部分,这与临床结果一致。
结论:所有这些结果表明T1Gd可以改善PCNSL患者的预后预测。据我们所知,这是首次使用DL解释PCNSL患者OS分类中的模型模式。未来的工作将涉及收集更多PCNSL患者的数据,或针对不同罕见疾病患者人群的其他回顾性研究,进一步推广我们模型的临床作用。
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.