初级中枢神经系统淋巴瘤的结局和复发模式:从1983年到2020年诊断的559名患者的纵向分析
Kathryn R Tringale1,2, Michael Scordo3, Joachim Yahalom1
1Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Neuro-oncology
|June 25, 2024
概括
主要中枢神经系统淋巴瘤 (PCNSL) 复发模式显示远程,不可预测的复发后完整的反应. 耐药PCNSL患者面临局部复发的更高风险,指导优化多模式治疗.
科学领域:
- 神经瘤学神经瘤学
- 血液学 血液学 血液学
- 临床结果研究研究的临床结果.
背景情况:
- 主要中枢神经系统淋巴瘤 (PCNSL) 的当代结果和复发模式尚未明确.
- 分析了大量随访的大量队列,以确定与PCNSL复发相关的因素.
研究的目的:
- 描述初级中枢神经系统淋巴瘤当代结果和复发模式.
- 确定影响PCNSL患者复发的因素.
主要方法:
- 从1983年到2020年对免疫能力强的PCNSL (扩散大B细胞) 患者的分析.
- 根据诱导和巩固的反应分层,分类最初的复发地点.
- 评估无进展生存率 (PFS) 和整体生存率 (OS) 使用比例危险和累积发病率与局部复发的竞争风险.
主要成果:
- 559名患者的中位随访时间为7.4年;大多数患者具有特定的人口和疾病特征 (例如, supratentorial,多焦点参与).
- 实现完整反应 (CR/CRu) 的患者在巩固后的1年和5年的PFS分别为80%和46%,局部复发率低 (1.8%在1年).
- 耐药患者 (n=97) 的1年局部复发率 (57%) 与非局部复发率 (42%) 相比显著更高,与局部复发相关的深层结构参与.
结论:
- 这项研究为大型PCNSL队列提供了全面的复发模式.
- 完整反应后复发通常是遥远的和不可预测的.
- 耐药性PCNSL患者表现出局部复发的高发病率,这表明对这一高风险群体需要优化多模式治疗.
更多相关视频
15:07VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
Published on: December 28, 2015
26.7K
10:18From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
Published on: October 19, 2014
13.7K
相关概念视频
Cancer Survival Analysis
342
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
342
Assumptions of Survival Analysis
121
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
121
