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使用机器学习和深度学习模型预测膀癌的预后和免疫微环境评估

Weihao Nie¹, Yiheng Jiang¹, Luhan Yao²

¹Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, 116021, China.

|December 17, 2024

概括

这项研究引入了与免疫相关的机器学习签名 (IRMLS) 来分类膀癌 (BCa) 亚型. 该IRMLS预测患者的生存率,并确定那些可能从免疫疗法和化疗中受益的人.

科学领域:

在瘤学瘤学.
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

膀癌 (BCa) 呈现出由瘤透免疫细胞驱动的多种免疫亚型.
了解瘤免疫微环境 (TIME) 对BCa预后和治疗策略至关重要.

研究的目的:

为BCa.开发和验证与免疫相关的机器学习签名 (IRMLS).
为了将IRMLS亚型与患者的生存率和治疗反应相关联.
利用深度学习从病态图像中检测IRMLS.

主要方法:

集成了10个机器学习算法来创建IRMLS.
使用病理图像开发了用于IRMLS亚型分类的深度学习模型.
分析了与IRMLS有关的癌症免疫循环和突变景观.

主要成果:

在BCa (p < 0.01) 中,IRMLS是总生存期 (OS) 的独立预后因素.
高风险的IRMLS群体表现出免疫炎症的表型,预后较差,免疫细胞透率增加.
高危人群对免疫检查点免疫疗法 (ICI) 和特定化疗 (cisplatin, docetaxel, etoposide, paclitaxel) 的敏感性较高.

结论:

IRMLS是一个验证的分子特征,反映了BCa生物学.
IRMLS为膀癌患者提供了个性化精密治疗的见解.
该研究强调了机器学习在理解BCa TIME和指导治疗决策方面的潜力.

关键词:

膀癌是一种癌症.深度学习是一种深度学习.免疫子类型免疫子类型免疫治疗是一种免疫疗法.机器学习是机器学习.瘤透的免疫细胞进入了瘤.整个幻灯片图像的图像.

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