PDF(Pubmed)
Abstract：
UNASSIGNED: The prognostic roles of clinical and laboratory markers have been exploited to model risk in patients with primary CNS lymphoma, but these approaches do not fully explain the observed variation in outcome. To date, neuroimaging or molecular information is not used. The aim of this study was to determine the utility of radiomic features to capture clinically relevant phenotypes, and to link those to molecular profiles for enhanced risk stratification.
UNASSIGNED: In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.
UNASSIGNED: The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.
UNASSIGNED: The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.
UNASSIGNED: In this retrospective study, we investigated 133 patients across 9 sites in Austria (2005-2018) and an external validation site in South Korea (44 patients, 2013-2016). We used T1-weighted contrast-enhanced MRI and an L1-norm regularized Cox proportional hazard model to derive a radiomic risk score. We integrated radiomic features with DNA methylation profiles using machine learning-based prediction, and validated the most relevant biological associations in tissues and cell lines.
UNASSIGNED: The radiomic risk score, consisting of 20 mostly textural features, was a strong and independent predictor of survival (multivariate hazard ratio = 6.56 [3.64-11.81]) that remained valid in the external validation cohort. Radiomic features captured gene regulatory differences such as in BCL6 binding activity, which was put forth as testable treatment target for a subset of patients.
UNASSIGNED: The radiomic risk score was a robust and complementary predictor of survival and reflected characteristics in underlying DNA methylation patterns. Leveraging imaging phenotypes to assess risk and inform epigenetic treatment targets provides a concept on which to advance prognostic modeling and precision therapy for this aggressive cancer.
摘要：
■临床和实验室标志物的预后作用已被用来模拟原发性中枢神经系统淋巴瘤患者的风险，但是这些方法不能完全解释观察到的结果变化。迄今为止,不使用神经成像或分子信息。这项研究的目的是确定放射学特征捕获临床相关表型的实用性。并将其与分子谱联系起来，以加强风险分层。
■在这项回顾性研究中，我们调查了奥地利9个地点(2005-2018年)和韩国一个外部验证地点的133名患者(44名患者，2013-2016)。我们使用T1加权对比增强MRI和L1范数正则化Cox比例风险模型得出放射学风险评分。我们使用基于机器学习的预测将放射学特征与DNA甲基化谱相结合，并验证了组织和细胞系中最相关的生物学关联。
■放射学风险评分，由20个主要是纹理特征组成，是一个强有力的独立生存预测因子(多变量风险比=6.56[3.64-11.81]),在外部验证队列中仍然有效.放射学特征捕获了基因调控差异，如BCL6结合活性，这被作为部分患者的可测试治疗目标。
■放射组学风险评分是生存的可靠和互补预测因子，并反映了潜在DNA甲基化模式的特征。利用成像表型来评估风险并告知表观遗传治疗目标，提供了一个概念，可以促进这种侵袭性癌症的预后建模和精确治疗。
■在这项回顾性研究中，我们调查了奥地利9个地点(2005-2018年)和韩国一个外部验证地点的133名患者(44名患者，2013-2016)。我们使用T1加权对比增强MRI和L1范数正则化Cox比例风险模型得出放射学风险评分。我们使用基于机器学习的预测将放射学特征与DNA甲基化谱相结合，并验证了组织和细胞系中最相关的生物学关联。
■放射学风险评分，由20个主要是纹理特征组成，是一个强有力的独立生存预测因子(多变量风险比=6.56[3.64-11.81]),在外部验证队列中仍然有效.放射学特征捕获了基因调控差异，如BCL6结合活性，这被作为部分患者的可测试治疗目标。
■放射组学风险评分是生存的可靠和互补预测因子，并反映了潜在DNA甲基化模式的特征。利用成像表型来评估风险并告知表观遗传治疗目标，提供了一个概念，可以促进这种侵袭性癌症的预后建模和精确治疗。
