Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

中文翻译：

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基于RNA序列数据的基于深度学习的癌症生存预后：方法和评估。

基于内核的深度学习模型的最新进展引入了医学研究的新时代。深度学习模型最初设计用于模式识别和图像处理，现已应用于癌症患者的生存预后。具体而言，使用转录组学数据训练了Cox比例风险模型的深度学习版本，以预测癌症患者的生存结果。在这项研究中，我们使用多种基于深度学习的模型对TCGA癌症进行了广泛的分析，包括Cox-nnet，DeepSurv和我们小组提出的名为AECOX（带有Cox回归网络的AutoEncoder）的方法。对数秩检验的一致性指数和p值用于评估模型性能。所有模型均显示出针对12种癌症的竞争结果。深度学习方法的最后隐藏层是输入数据的低维表示，可用于特征约简和可视化。此外，预后表现揭示了模型准确性，总生存时间统计数据和肿瘤突变负担（TMB）之间的负相关，表明总生存时间，TMB和预后预测准确性之间存在关联。与传统的基于机器学习的模型相比，基于深度学习的算法展示了卓越的性能。以一致性指数衡量的癌症预后结果在各个模型之间是无法区分的，而在各个癌症之间却存在很大差异。这些发现为泛癌水平上的患者特征与生存学习能力之间的关系提供了一些启示。