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不同恶性转变模式在扩散性低度质瘤中的特征和临床影响

Haihui Jiang¹, Qinghui Zhu², Xijie Wang²

¹Department of Neurosurgery, Peking University Third Hospital, Peking University, Beijing, China.

|June 19, 2023

概括

预测低度质瘤 (LGGs) 恶性转变至关重要. 这项研究确定了关键因素,如1p/19q代选择,Ki-67指数,放射治疗,切除程度和KPS得分,以预测进展并改善患者的治疗结果.

科学领域:

神经瘤学神经瘤学
癌症进展研究的研究.
临床数据分析临床数据分析

背景情况:

低度质瘤 (LGGs) 的恶性转化 (MT) 到更高度质瘤是一个重要的临床挑战.
预测哪些患者会有进展,什么时候仍不清楚,影响治疗策略.

研究的目的:

调查LGG中不同MT模式的特征.
在LGG患者中开发MT的预测模型.
确定影响MT早期干预的因素.

主要方法:

对229名老年患者进行了回顾性队列研究,这些患者患有复发性LGG.
根据病变的进展,将患者分为MT组 (2-2,2-3,2-4),进行分类.
多变量后勤回归和生存分析,以确定独立的预测因素.
开发和验证用于MT预测的nomogram模型.

主要成果:

接受MT治疗的患者表现出明显的临床和分子特征,包括较低的卡诺夫斯基性能量表 (KPS) 评分,较大的瘤大小,较小的切除范围 (EOR),较高的Ki-67指数和较低的1p/19q代选择率.
独立的MT预测因素包括1p/19q代选择,Ki-67指数,放射治疗,EOR和KPS得分.
纳入这些因素的诺莫格拉姆模型在预测MT时表现出高准确度 (0.843),表现优于进展后增强 (PPE).

结论:

最初的诊断因素,如1p/19q代选择,Ki-67指数,放射治疗,EOR和KPS得分,可以准确地预测LGG患者随后的MT模式.
开发的诺米图为早期MT预测提供了有价值的工具,有可能指导治疗决策并改善患者管理.
了解这些进展模式对于优化LGG管理中的治疗策略至关重要.

关键词:

低度质瘤是一种低度的质瘤.恶性转变的恶性转变数学模型是一个数学模型.预测性标记是预测性的标记.预后预后预后

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