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人工智能和成熟的淋巴瘤瘤的分类

Joaquim Carreras¹, Rifat Hamoudi^2,3, Naoya Nakamura¹

¹Department of Pathology, Tokai University School of Medicine, Isehara 259-1193, Japan.

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概括

通过结合临床,组织学和分子数据,更新了淋巴瘤瘤的分类. 人工智能在预测患者预后和使用细胞起源标记物分类淋巴瘤亚型方面表现有前途.

科学领域:

血液学血液学血液学
在瘤学瘤学.
遗传学遗传学是一个
生物信息学是一种生物信息学.

背景情况:

达成了关于对淋巴瘤瘤进行分类的多学科协议.
目前的分类包括世卫组织 (4th/5th版). 和国际共识分类 (ICC).
人工智能 (AI) 的进步正在影响医学预测和分类.

研究的目的:

修订最近在成熟淋巴瘤瘤的分类方面的进展.
探索AI在淋巴瘤分类和预后预测中的作用.
为了比较当前的分类系统,并识别差异.

主要方法:

审查当前的淋巴瘤瘤分类 (世卫组织,ICC).
分析人工智能应用,包括机器学习和神经网络,用于预后和分类.
使用细胞起源标记物 (例如CD3,CD5,CD19,CD20) 评估淋巴瘤亚型预测.

主要成果:

大多数类别在世卫组织和ICC分类之间是相似的,但存在概念和诊断差异.
人工智能模型可以预测患者的预后,并对成熟的B细胞瘤进行分类.
淋巴瘤亚型可以使用已建立的细胞起源标记物来预测.

结论:

尽管存在相似之处,但当前的淋巴瘤瘤分类有不同的标准.
人工智能正在成为用于淋巴瘤分类的宝贵生物信息学工具.
预计未来将AI整合到淋巴瘤分类中.

关键词:

人工智能的人工智能是人工智能.人工神经网络的人工神经网络深度学习是一种深度学习.基因表达的基因表达方式机器学习是机器学习.其他非霍奇金淋巴瘤.胰腺癌系列的癌症.预后预后预后

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