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基于临床和基因表达数据的机器学习有助于在扩散大B细胞淋巴瘤患者的生存预测和治疗优化

Junwei Lin^1,2,3, Weifeng Lv^1,2,3, Huixing Cai^1,2

¹The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, China.

Annals of hematology

|February 21, 2026

概括

我们开发了一个机器学习模型,使用临床和基因表达数据来预测扩散大B细胞淋巴瘤 (DLBCL) 的存活率. 这种工具可以改善预后预测,并有助于为这种侵袭性非霍奇金淋巴瘤亚型选择个性化治疗.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

扩散性大B细胞淋巴瘤 (DLBCL) 是一种常见且具有攻击性的非霍奇金淋巴瘤 (NHL).
目前DLBCL的预后工具在个性化预测方面存在局限性.
对异质DLBCL的治疗选择仍然是一个临床挑战.

研究的目的:

开发一种机器学习模型,以改善DLBCL的预后预测.
整合临床和基因表达数据,以提高DLBCL患者分层.
为个性化DLBCL治疗选择创建一个决策支持工具.

主要方法:

利用随机生存森林模型与来自677例DLBCL病例的临床和基因表达数据 (基因表达综合数据库).
使用一致性指数 (C指数) 和时间依赖ROC曲线验证模型性能.
使用卡普兰-梅尔曲线,多变量分析和惩罚性考克斯回归识别了预后基因.

主要成果:

开发的模型显示了高预测总生存率的准确性 (C指数:0.832培训,0.758验证),表现优于现有标记.
该模型对1年,3年和5年的生存预测表现良好.
接受最佳治疗的患者表现出明显更好的生存结果.
六个关键基因 (C2CD5,CD163,JADE3,BIRC3,TMEM200A,LINC00877) 被确定为预后指标.

结论:

整合临床和基因表达数据的机器学习模型提供了可靠的DLBCL预后预测.
该模型是DLBCL中个性化治疗选择的有价值的决策支持工具.
这种方法增强了针对侵袭性非霍奇金淋巴瘤的个性化医疗策略.

关键词:

扩散性大B细胞淋巴瘤机器学习是机器学习.预测生存的预测.优化治疗的优化处理.

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