Abstract:
The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.
摘要:
准确预测巴塞罗那临床肝癌(BCLC)B期肝细胞癌(HCC)患者的术后生存时间对术后保健具有重要意义。生存分析是医学领域中用于预测感兴趣事件发生时间的常用方法。目前,主流生存分析模型,比如Cox比例风险模型,应该对潜在的随机过程做出严格的假设来解决删失数据,从而潜在地限制了它们在临床实践中的应用。在本文中,我们提出了一种新的深度多任务生存模型(DMSM)来分析HCC生存数据。具体来说,DMSM将传统的HCC患者生存时间预测问题转化为多个时间点的生存概率预测问题,并应用熵正则化和排序损失来优化多任务神经网络。与传统删除删失数据和强假设的方法相比,DMSM充分利用了删失数据中的所有信息,但不需要做任何假设。此外,我们确定了影响HCC患者预后的危险因素,并显示了对这些因素进行排序的重要性.在分析BCLCB期HCC患者的真实数据集的基础上,在三个不同的验证数据集上的实验结果表明,DMSM的一致性指数分别为0.779、0.727和0.780,综合Brier评分(IBS)分别为0.172、0.138和0.135。我们的DMSM对于自举100次的IBS具有相对较小的标准偏差(0.002、0.002和0.003)。我们提出的DMSM可以作为一种有效的生存分析模型,为BCLCB期HCC患者术后生存时间的准确预测提供重要手段。