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在前列腺癌患者中,基于机器学习的生物标志物和病理学特征的预后和预测价值.

Jianpeng Zhang1,2,3,4, Jinyou Pan1,2,3,4, Jingwei Lin1

  • 1Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

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概括

新的机器学习模型,转移相关预后风险评分 (MAPRS) 和病理学评分 (PSpc),有效预测前列腺癌在手术后复发. 这些得分提供了对治疗敏感性和瘤异质性的见解.

关键词:
生物标志物生物标志物机器学习是机器学习.病理学 是一种病理学.预测模型的预测模型.前列腺癌是前列腺癌.

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 数字病理学数字病理学

背景情况:

  • 针对前列腺癌 (PCa) 的激进前列腺切除术 (RP) 面临复发和割抗性的挑战.
  • 现有的PCa预后模型缺乏广泛的临床应用.
  • 了解分子和形态异质性对于准确的PCa预后至关重要.

研究的目的:

  • 开发和验证基于机器学习的新型预后得分,用于预测PCa患者在RP后的无复发生存期 (RFS).
  • 评估转录组衍生 (MAPRS) 和数字病理衍生 (PSpc) 评分在预测PCa结果中的实用性.
  • 探索这些得分与瘤微环境,遗传变异和治疗敏感性的关联.

主要方法:

  • 在多中心转录组数据上利用了五种机器学习算法 (LASSO,RSF,SVM-RFE,Boruta,XGBoost) 来开发转移相关预后风险评分 (MAPRS).
  • 在H&E染色的数字病理学图像上使用机器学习框架 (XGBoost,RSF,GBM,plsRCox,CoxBoost,Enet,Ridge,LASSO,SVM,superPC) 来得出病理学分数 (PSpc).
  • 使用内部病理学样本验证了MAPRS和PSpc,并评估了它们对RFS和无进展生存期 (PFS) 的预测性表现.

主要成果:

  • MAPRS有效地预测了较差的RFS,并与瘤微环境和致病变体有关.
  • 较高的MAPRS表明对PARP抑制剂,多塞和氧沙等治疗方法的潜在敏感性.
  • 对MAPRS和PSpc的综合评估表明,与单个分数或其他工具相比,RP后RFS的预测优越.
  • MAPRS还预测了在接受雄激素剥夺治疗的患者的PFS,而PSpc在这种情况下显示出有限的疗效.

结论:

  • 基于机器学习的预后签名,特别是来自数字病理学的预后签名,为PCa复发提供了卓越的预测效率.
  • MAPRS和PSpc为分子和形态瘤异质性提供了宝贵的见解,有助于风险分层.
  • 这些新的得分有望改善前列腺癌管理中的临床决策和个性化治疗策略.