基于神经网络的癌症诊断从常规的血液学和生物化学数据:性能,目标泄漏和临床影响.
Jehad F Alhmoud1, Moath Alqaraleh2, Futoon Abedrabbu Al-Rawashdea2
1Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan.
概括
常规血液检查和瘤标志物对已经接受专家评估的患者的癌症预测具有有限的价值. 癌症患者的血红蛋白水平较低,但不是特定的诊断指标.
科学领域:
- 在瘤学瘤学.
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的机器学习
背景情况:
- 常规血液检查和瘤标志物在瘤治疗中常见,用于潜在的恶性瘤.
- 机器学习 (ML) 方法越来越多地被用于使用这些数据进行自动化癌症预测.
- 这些熟悉的标记物在专业路径中的预测实用性和目标泄漏的风险需要仔细考虑.
研究的目的:
- 调查基本的人口,血液学,生化和临床变量与癌症诊断在癌症丰富队列之间的关系.
- 评估这些关系对基于神经网络的癌症预测模型的影响.
主要方法:
- 进行了一项二次的,分析性的横截面研究,使用来自癌症风险分层使用实验室参数数据集的1000例.
- 提取的数据包括人口统计,吸烟状况,家族史,全血细胞计,血糖,瘤标志物 (CA-125,PSA,CEA),癌症阶段和生存率.
- 进行神经网络分析以评估癌症状况 (癌症与没有癌症).
主要成果:
- 该队列包括超过80%的患有恶性病的患者.
- 大多数常规实验室和生物化学值在癌症和非癌症组之间没有显著差异,保持在常规参考范围内.
- 癌症患者的血红蛋白水平略低但显著降低;癌症状况与癌症阶段有很强的相关性,与生存率有很弱的相关性.
结论:
- 在被引用的癌症丰富人群中,临床风险因素和个人常规实验室参数提供了超出癌症阶段存在的最小额外诊断歧视.
- 血红蛋白是一般疾病的非特异性指标,而不是癌症的特定诊断标记.
- 使用这些变量用于基于ML的癌症预测时,需要仔细建模,以避免目标泄漏.
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