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在前列腺癌中淋巴结入侵预测:比较机器学习研究

Osman Can1, Özgün Yücel2, Yiğit Can Filtekin3

  • 1Urology Department, Basaksehir Cam and Sakura City Hospital, Başakşehir Neighborhood, G-434 Street, No: 2L, 34480, Başakşehir, Istanbul, Türkiye. dr.osmancan01@gmail.com.

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概括

机器学习模型在前列腺癌中准确预测淋巴结入侵 (LNI),表现优于传统的诺米图. 这些模型,特别是Random Forest,通过更好地识别LNI风险,改善了手术决策.

关键词:
盗窃者 盗窃者 盗窃者 盗窃者 盗窃者我们的MSKCCCC是MSKCC.机器学习是机器学习.在Partin的部分.前列腺癌是什么意思 前列腺癌是什么意思随机的森林 随机的森林

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科学领域:

  • 泌尿器科 泌尿器科 泌尿器科 泌尿器科
  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学

背景情况:

  • 在前列腺癌中精确的淋巴结入侵 (LNI) 的术前预测对于手术规划至关重要.
  • 现有的诺姆图试图平衡缺失LNI和不必要的淋巴结解剖的风险.
  • 机器学习 (ML) 为复杂的临床数据提供了增强的预测能力.

研究的目的:

  • 开发和评估ML模型来预测前列腺癌中的LNI.
  • 为了比较ML模型的性能与传统的名ograms.
  • 为了提高模型的解释性,使用夏普利添加式扩展 (SHAP).

主要方法:

  • 开发了使用临床病理特征来预测LNI的ML模型.
  • 应用合成少数群体过量采样技术 (SMOTE) 解决阶级不平衡问题.
  • 使用十倍交叉验证训练和评估了四个ML算法 (k-NN,随机森林,SVM,XGBoost).
  • 通过精度,灵敏度,特异性和AUC评估性能;使用SHAP进行解释性.

主要成果:

  • 随机森林模型表现出最高的预测性能.
  • 关键预测因素包括PSA密度,临床阶段和cribriform模式.
  • SHAP分析提供了功能贡献的可视化.
  • 随机森林和XGBoost表现出优越的歧视与现有的nomograms相比.

结论:

  • 机器学习模型在预测前列腺癌方面显示出有可能超越传统的诺米图. LNI.
  • 像SHAP这样的数据集平衡和可解释性工具对于ML模型的有效性至关重要.
  • 外部验证和整合额外的功能可以提高模型的通用性.