论文标题
Deep-Cr MTLR：一种具有竞争风险的癌症生存预测的多模式方法
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
论文作者
论文摘要
准确的生存预测对于开发精确的癌症医学至关重要，从而需要新的预后信息来源。最近，人们对利用常规收集的临床和医学成像数据有很大的兴趣，以发现多种癌症类型的新预后标记。但是，以前的大多数研究都只关注单个数据模式，并且不利用机器学习的最新进展来进行生存预测。我们提出了Deep-CR MTLR-一种新型的机器学习方法，用于基于神经网络的竞争风险，从多模式临床和成像数据中进行准确的癌症存活预测，并扩展了多任务逻辑回归框架。我们证明，在2552头和颈癌患者的队列中，多模式方法的预后性能改善了单模式预测因子，尤其是对于癌症特异性生存，我们的方法将2年的AUROC达到0.774和$ c $ index的0.788。
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-modal clinical and imaging data in the presence of competing risks based on neural networks and an extension of the multi-task logistic regression framework. We demonstrate improved prognostic performance of the multi-modal approach over single modality predictors in a cohort of 2552 head and neck cancer patients, particularly for cancer specific survival, where our approach achieves 2-year AUROC of 0.774 and $C$-index of 0.788.
