Cui Yuning, Li Yifu, Pan Chongle, Brown Stephanie R, Gallant Rachel E, Zhu Rui
School of Industrial and Systems Engineering, The University of Oklahoma, Norman, OK, 73019, USA.
School of Computer Science, The University of Oklahoma, Norman, OK, 73019, USA.
Comput Biol Med. 2023 Apr;156:106713. doi: 10.1016/j.compbiomed.2023.106713. Epub 2023 Feb 28.
Childhood Leukemia is the most common type of cancer among children. Nearly 39% of cancer-induced childhood deaths are attributable to Leukemia. Nevertheless, early intervention has long been underdeveloped. Moreover, there are still a group of children succumbing to their cancer due to the cancer care resource disparity. Therefore, it calls for an accurate predictive approach to improve childhood Leukemia survival and mitigate these disparities. Existing survival predictions rely on a single best model, which fails to consider model uncertainties in predictions. Prediction from a single model is brittle, with model uncertainty neglected, and inaccurate prediction could lead to serious ethical and economic consequences.
To address these challenges, we develop a Bayesian survival model to predict patient-specific survivals by taking model uncertainty into account. Specifically, we first develop a survival model predict time-varying survival probabilities. Second, we place different prior distributions over various model parameters and estimate their posterior distribution with full Bayesian inference. Third, we predict the patient-specific survival probabilities changing with respect to time by considering model uncertainty induced by posterior distribution.
Concordance index of the proposed model is 0.93. Moreover, the standardized survival probability of the censored group is higher than that of the deceased group.
Experimental results indicate that the proposed model is robust and accurate in predicting patient-specific survivals. It can also help clinicians track the contribution of multiple clinical attributes, thereby enabling well-informed intervention and timely medical care for childhood Leukemia.
儿童白血病是儿童中最常见的癌症类型。近39%因癌症导致的儿童死亡可归因于白血病。然而，长期以来早期干预一直未得到充分发展。此外，仍有一群儿童因癌症护理资源差异而死于癌症。因此，需要一种准确的预测方法来提高儿童白血病的生存率并减轻这些差异。现有的生存预测依赖于单一的最佳模型，该模型未能考虑预测中的模型不确定性。单一模型的预测很脆弱，忽略了模型不确定性，不准确的预测可能会导致严重的伦理和经济后果。
为应对这些挑战，我们开发了一种贝叶斯生存模型，通过考虑模型不确定性来预测患者特定的生存率。具体而言，我们首先开发一个生存模型来预测随时间变化的生存概率。其次，我们对各种模型参数设置不同的先验分布，并通过全贝叶斯推理估计其后验分布。第三，我们通过考虑后验分布引起的模型不确定性来预测患者特定的生存概率随时间的变化。
所提出模型的一致性指数为0.93。此外，截尾组的标准化生存概率高于死亡组。
实验结果表明，所提出的模型在预测患者特定的生存率方面是稳健且准确的。它还可以帮助临床医生跟踪多种临床属性的贡献，从而为儿童白血病提供明智的干预和及时的医疗护理。
