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对以前未经治疗的扩散性大B细胞淋巴瘤早期复发和进展的预后因素的调查以及POD12的统计预测模型

Ke Lian¹, Wenyao Zhu², Zhihui Hu¹

¹Department Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.

Frontiers in immunology

|August 27, 2025

概括

在扩散性大B细胞淋巴瘤 (DLBCL) 中,12个月内疾病的进展显著影响了生存率. 贝塔2微球蛋白和ECOG性能状态是POD12的关键独立风险因素,为预后模型提供信息.

科学领域:

血液学
癌症学
医疗信息学

背景情况:

扩散性大B细胞淋巴瘤 (DLBCL) 是一种侵袭性的非霍奇金淋巴瘤.
早期识别患有疾病进展高风险的患者对于优化治疗至关重要.
疾病在12个月内进展 (POD12) 是治疗反应和患者结果的关键指标.

研究的目的:

在DLBCL患者中调查POD12的发病率,预后意义和风险因素.
使用临床数据和先进的机器学习技术开发POD12的预测模型.

主要方法:

对69例DLBCL病例的回顾性分析.
用物流回归和考克斯回归分析来确定POD12的危险因素.
使用卷积神经网络-长期短期记忆 (CNN-LSTM) 和粒子群优化-一般回归神经网络 (PSO-GRNN) 的预测模型的开发.

主要成果:

POD12与无进展生存期 (PFS) 和整体生存期 (OS) 有显著的相关性.
POD12的独立风险因素包括β2MG和东部合作性瘤组 (ECOG) 的性能状况.
一个包含LDH,β2MG,阶段,ECOG,NLR和SII的预测模型表现良好 (AUC=0.846).
CNN-LSTM和PSO-GRNN模型显示适用于预测POD12风险.

结论:

POD12是DLBCL的一个重要预后因素.
β2MG和ECOG性能状态是POD12的独立预测指标.
机器学习模型,特别是CNN-LSTM和PSO-GRNN,对预测DLBCL患者的POD12风险充满希望.

关键词:

扩散性大B细胞淋巴瘤早期的进展 POD12的预测模型预后情况风险因素

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