Table_1_Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.docx

BackgroundAccurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility.
Materials and methodsPatients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms—two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])—were selected for training. Meanwhile, a multivariate Cox proportional hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into training and testing datasets at a ratio of 7:3. Hyperparameter tuning was conducted through a 1000-repeated random search with 5-fold cross-validation on the training dataset. The model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-, 3-, 5- and 10-year survival was evaluated using receiver operating characteristic curves (ROC), calibration curves, and the area under the ROC curves (AUC).
ResultsA total of 3145 patients were finally enrolled in our study. The mean age at diagnosis was 52 ± 18 years, 1662 of the 3145 patients were male (53%), and mean survival time was 83 ± 67 months. Two deep learning models outperformed the RSF and classical CoxPH models, with the C-index on test datasets achieving values of 0.832 (DeepSurv) and 0.821 (NMTLR). The DeepSurv model produced better accuracy and calibrated survival estimates in predicting 1-, 3- 5- and 10-year survival (AUC:0.895-0.937). We deployed the DeepSurv model as a web application for use in clinical practice; it can be accessed through https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py.
ConclusionsTime-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.

背景 准确预测软骨肉瘤患者的预后，对其治疗决策制定至关重要。目前已有多项研究采用多变量Cox回归（multivariate Cox regression）或基于二元分类的机器学习方法，构建软骨肉瘤患者3年及5年生存率的预测模型，但鲜有研究探索将深度学习与生存时间预测相结合的模型效果。相较于将预测问题简化为二元分类任务，将事件发生概率建模为时间的函数并结合深度学习，能够获得更优的预测精度与灵活性。
材料与方法 本研究从监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）登记数据库中提取2000年至2018年间确诊为软骨肉瘤的患者数据。本研究选取三种算法开展模型训练：两种基于神经网络的算法（DeepSurv、神经网络多任务逻辑回归（neural multi-task logistic regression, NMTLR）），以及一种基于集成学习的算法（随机生存森林（random survival forest, RSF））。同时构建多变量Cox比例风险（Cox proportional hazards, CoxPH）模型作为对照。将数据集以7:3的比例随机划分为训练集与测试集。在训练集上通过1000次重复随机搜索结合5折交叉验证完成超参数调优。采用一致性指数（concordance index, C-index）、Brier评分以及综合Brier评分（Integrated Brier Score, IBS）评估模型性能。通过受试者工作特征曲线（receiver operating characteristic curve, ROC）、校准曲线以及ROC曲线下面积（area under the ROC curves, AUC），评估模型对1年、3年、5年及10年生存率的预测精度。
结果 本研究最终纳入3145例患者。确诊时的平均年龄为52±18岁，其中1662例为男性（占比53%），平均生存时间为83±67个月。两款深度学习模型的性能均优于RSF与经典CoxPH模型，测试集上的一致性指数分别为0.832（DeepSurv）与0.821（NMTLR）。DeepSurv模型在预测1年、3年、5年及10年生存率时，展现出更优的预测精度与校准后的生存估计结果（AUC：0.895~0.937）。本研究将DeepSurv模型部署为可供临床使用的Web应用程序，访问地址为https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py。
结论 基于深度学习算法的生存时间预测模型，在软骨肉瘤预后预测中表现优异，其中DeepSurv模型拥有最佳的区分度与校准性能。