BackgroundOvarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best‐performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models.MethodsOur data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change‐point detection and recurrent neural networks.ResultsWe obtained a significantly higher performance for CA125–HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change‐point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125–HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125.ConclusionsOur study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

中文翻译：

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贝叶斯和深度学习模型应用于使用多个纵向生物标志物早期检测卵巢癌

背景卵巢癌是所有妇科癌症中死亡率最高的。癌症抗原 125 (CA125) 是效果最好的卵巢癌生物标志物，但作为普通人群的筛查测试仍然无效。最近的文献报道了使用纵向多标记物模型时有可能改进 CA125 进行早期检测的其他生物标记物。方法我们的数据包括 180 名对照者和 44 例血清样本，其血清样本来自英国卵巢癌筛查协作试验 (UKCTOCS) 的多模式部门。我们的模型基于贝叶斯变化点检测和循环神经网络。结果我们使用两种方法（AUC 0.971，灵敏度 96.7% 和 AUC 0.987，灵敏度 96.7%）获得了 CA125-HE4 模型相对于 CA125 (AUC贝叶斯变点模型 (BCP) 和循环神经网络 (RNN) 方法的 AUC 分别为 0.949，敏感性 90.8% 和 AUC 0.953，敏感性 92.1%。诊断前一年，CA125-HE4 模型也被评为最佳，而在诊断前 2 年，没有多标志物模型优于 CA125。结论我们的研究使用纵向多变量模型识别并测试了不同的生物标志物组合，其性能优于单独的 CA125。我们展示了多变量模型和候选生物标志物在提高卵巢癌检出率方面的潜力。