Yan Lizhao, Gao Nan, Ai Fangxing, Zhao Yingsong, Kang Yu, Chen Jianghai, Weng Yuxiong
Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Orthopaedics, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2022 Aug 22;12:967758. doi: 10.3389/fonc.2022.967758. eCollection 2022.
Accurate 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.
Patients 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).
A 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.
Time-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.
准确预测预后对于软骨肉瘤患者的治疗决策至关重要。已经利用多变量Cox回归或基于二元分类的机器学习方法创建了几种预后模型，以预测软骨肉瘤患者的3年和5年生存率，但很少有研究探讨将深度学习与事件发生时间预测相结合的结果。与将预测简化为二元分类问题相比，通过将事件概率建模为时间的函数并结合深度学习可以提供更高的准确性和灵活性。
从监测、流行病学和最终结果（SEER）登记处提取2000年至2018年间诊断为软骨肉瘤的患者。选择三种算法——两种基于神经网络（深度生存模型[DeepSurv]、神经多任务逻辑回归[NMTLR]）和一种基于集成学习（随机生存森林[RSF]）——进行训练。同时，还构建了一个多变量Cox比例风险（CoxPH）模型进行比较。数据集以7:3的比例随机分为训练集和测试集。通过在训练数据集上进行1000次重复随机搜索和5折交叉验证来进行超参数调整。使用一致性指数（C指数）、Brier评分和综合Brier评分（IBS）评估模型性能。使用受试者工作特征曲线（ROC）、校准曲线和ROC曲线下面积（AUC）评估预测1年、3年、5年和10年生存率的准确性。
最终共有3145例患者纳入本研究。诊断时的平均年龄为52±18岁，3145例患者中有1662例为男性（53%），平均生存时间为83±67个月。两种深度学习模型优于RSF和经典CoxPH模型，测试数据集上的C指数分别为0.832（DeepSurv）和0.821（NMTLR）。DeepSurv模型在预测1年、3年、5年和10年生存率方面具有更好的准确性和校准生存估计（AUC：0.895 - 0.937）。我们将DeepSurv模型部署为一个网络应用程序用于临床实践；可通过https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py访问。
基于深度学习算法的事件发生时间预测模型成功地预测了软骨肉瘤的预后，其中DeepSurv具有最佳的判别性能和校准效果。
