中枢神经系统淋巴瘤（CNSL）表现出明显的临床异质性，但准确预测结果仍具有挑战性。IPCG标准广泛应用于日常实践中以评估治疗反应。然而，IPCG标准在最终结果预测方面的价值在很大程度上尚不清楚，主要是由于在治疗期间和治疗后区分完全缓解和部分缓解的不确定性。我们探索了包括不同疾病里程碑的半自动化三维肿瘤体积测量在内的多种MRI特征，以及它们与接受治愈性治疗的93名CNSL患者的生存率的关联。在诊断时，具有超过三个淋巴瘤病灶、脑室周围侵犯和高三维肿瘤体积的患者显示出显著不利的无进展生存期（PFS）和总生存期（OS）。在治疗期间的第一次过渡期MRI中，IPCG标准未能区分反应患者的结果。因此，我们随机分配这些患者到训练组和验证组，以研究三维肿瘤体积测量是否能改善结果预测。我们确定了≥97%的三维肿瘤体积减小作为风险分层的最佳阈值（3D早期反应，3D_ER）。应用于验证组，达到3D_ER的患者具有明显优越的结果。在多元分析中，3D_ER是PFS和OS的独立预后因子。最后，我们利用3D MRI特征和循环生物标志物的预后信息建立了一个综合指标，进一步改善了CNSL的结果预测。我们开发了半自动化的三维肿瘤体积测量作为CNSL患者临床结果的强有力且独立的早期预测因子。这些放射学特征可以帮助改善风险分层并指导未来的治疗方法。 © 2023年作者。由牛津大学出版社代表神经肿瘤学学会出版。版权所有。有关权限，请发送电子邮件至：journals.permissions@oup.com。

Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment.We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment.At diagnosis, patients with more than three lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL.We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.