更简单的预测模型为卵巢癌检测提供了更高的准确性
Derrick E Wood1, Joseph Roy1,2,3, Bari J Ballew1
1Blackjack Biotechnologies, Baltimore, MD, United States of America.
PeerJ
|December 22, 2025
概括
使用无细胞DNA (cfDNA) 和蛋白质生物标志物的卵巢癌查可能不会提高准确性. 结合CA125和HE4蛋白质的简单模型显示性能与复杂的cfDNA模型相美.
科学领域:
- 在瘤学瘤学.
- 生物标志物发现发现
- 机器学习在医学中的应用
背景情况:
- 卵巢癌对女性健康构成重大威胁,需要改进查方法.
- 已知蛋白质生物标志物CA125和HE4在检测卵巢癌方面具有很高的准确性,特别是当它们结合使用时.
- 之前的研究引入了DELFI-Pro,这是一个逻辑回归 (LR) 模型,将无细胞DNA (cfDNA) 特征与蛋白质度相结合.
研究的目的:
- 通过解决其培训数据中的潜在混因素,重新评估DELFI-Pro选模型的有效性.
- 确定DELFI-Pro中的cfDNA特征是否在检测卵巢癌时比仅含蛋白质的生物标志物具有显著的优势.
主要方法:
- 在之前的DELFI-Pro研究中使用的数据集的分析,重点是cfDNA衍生特征和蛋白质度 (CA125,HE4).
- 识别和删除具有异常染色体复制数值的训练数据样本,可能引入技术变异.
- 使用交叉验证对精制的DELFI-Pro模型与仅蛋白质的逻辑回归分类器进行比较性性能评估.
主要成果:
- 在原始DELFI-Pro培训数据的cfDNA特征中发现了与之相冲突的技术变异.
- 从训练组中排除了42个异常癌症样本,发现DELFI-Pro的表现并没有超过仅蛋白质的模型.
- 合并的CA125和HE4蛋白模型实现了0.99的曲线下面积 (AUC),表明了高精度.
结论:
- 在DELFI-Pro中的cfDNA特征并没有提供足够的附加值,以证明它们在更简单的基于蛋白质的模型中被纳入.
- 更简单的机器学习模型,如仅蛋白质分类器,往往更好地对新数据进行概括.
- 目前的证据不充分支持复杂的DELFI-Pro模型用于卵巢癌查.
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