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基于机器学习的预测模型和前列腺癌的视觉解释.

Gang Chen1, Xuchao Dai1, Mengqi Zhang1

  • 1School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China.

BMC urology
|October 14, 2023
PubMed
概括

一个新的XGBoost模型使用常规临床数据提高了前列腺癌 (PCa) 预测准确度. 该模型结合了血清前列腺特异性抗原 (PSA) 和生物化学标记,为PCa诊断和查提供了宝贵的工具.

关键词:
生物化学参数 生物化学参数机器学习 机器学习前列腺癌是什么意思 前列腺癌是什么意思风险门是指一个风险门.谢普利的价值是什么意思

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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.
  • 生物医学信息学 生物医学信息学

背景情况:

  • 前列腺癌 (PCa) 诊断在很大程度上依赖于血清前列腺特异抗原 (PSA) 测试,但其准确性需要提高.
  • 开发具有高临床实用性的先进PCa预测模型对于改善患者的治疗结果至关重要.

研究的目的:

  • 开发和验证基于XGBoost的前列腺癌预测模型,使用随时可用的临床和生物化学参数.
  • 评估与传统的PSA标志物相比,开发的模型的诊断性能.

主要方法:

  • 来自中国国家临床医学科学数据中心的良性前列腺增生症 (BPH) 和PCa患者数据的回顾性分析.
  • 构建一个包含年龄,BMI,PSA参数和血清生物化学标记的XGBoost模型.
  • 使用决策分析曲线 (DCA) 和通过SHAP框架进行变量重要性分析来评估模型的临床效用.

主要成果:

  • 在XGBoost模型中,AUC达到0.82,超过了f/tPSA (0.75),tPSA (0.68) 和fPSA (0.61).
  • 自由与总PSA比率 (f/tPSA) 是最重要的预测因素,其次是无机 (P), (K),肌酸酶MB异酶 (CKMB),LDL-C和肌酸氨酸 (Cre).
  • 对于关键标志物的确定的PCa风险值是:f/tPSA (0.13),P (1.29 mmol/L),K (4.29 mmol/L),CKMB (11.6 U/L),LDL-C (3.05 mmol/L) 和Cre (74.5-99.1 umol/L) 等.

结论:

  • 开发的XGBoost模型显示出高临床实用性和广泛适用性,特别有利于资源有限的环境.
  • 生物化学标志物的确定的风险值可以显著帮助临床诊断和前列腺癌查.