Harbin Medical University, Harbin, 150001, China.
Department of Neurobiology, Harbin Medical University, Harbin, 150001, China.
J Cancer Res Clin Oncol. 2023 Sep;149(11):8935-8944. doi: 10.1007/s00432-023-04842-8. Epub 2023 May 8.
PURPOSE: We developed DeepSurv, a deep learning approach for predicting overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC). We validated and visualized the novel staging system based on DeepSurv using data from multiple cohorts. METHODS: Totally 6020 ESCC patients diagnosed from January 2010 to December 2018 were included in the present study from the Surveillance, Epidemiology, and End Results database (SEER), randomly assigned to the training and test cohorts. We developed, validated and visualized a deep learning model that included 16 prognostic factors; then a novel staging system was further constructed based on the total risk score derived from the deep learning model. The classification performance at 3-year and 5-year OS was assessed by the receiver-operating characteristic (ROC) curve. Calibration curve and the Harrell's concordance index (C-index) were also used to comprehensively assess the predictive performance of the deep learning model. Decision curve analysis (DCA) was utilized to assess the clinical utility of the novel staging system. RESULTS: A more applicable and accurate deep learning model was established, which outperformed the traditional nomogram in predicting OS in the test cohort (C-index: 0.732 [95% CI 0.714-0.750] versus 0.671 [95% CI 0.647-0.695]). The ROC curves at 3-year and 5-year OS for the model also showed good discrimination ability in the test cohort (Area Under the Curve [AUC] at 3-/5-year OS = 0.805/0.825). Moreover, using our novel staging system, we observed a clear survival difference among different risk groups (P < 0.001) and a significant positive net benefit in the DCA. CONCLUSIONS: A novel deep learning-based staging system was constructed for patients with ESCC, which performed a significant discriminability for survival probability. Moreover, an easy-to-use web-based tool based on the deep learning model was also implemented, offering convenience for personalized survival prediction. We developed a deep learning-based system that stages patients with ESCC according to their survival probability. We also created a web-based tool that uses this system to predict individual survival outcomes.
目的：我们开发了 DeepSurv，这是一种用于预测食管鳞状细胞癌（ESCC）患者总生存（OS）的深度学习方法。我们使用来自多个队列的数据验证和可视化了基于 DeepSurv 的新分期系统。 方法：本研究共纳入了 2010 年 1 月至 2018 年 12 月期间来自 Surveillance，Epidemiology，and End Results（SEER）数据库的 6020 名 ESCC 患者，随机分配到训练和测试队列中。我们开发、验证和可视化了一个包含 16 个预后因素的深度学习模型；然后，根据深度学习模型得出的总风险评分，进一步构建了一个新的分期系统。通过受试者工作特征（ROC）曲线评估 3 年和 5 年 OS 的分类性能。校准曲线和 Harrell 一致性指数（C-index）也用于全面评估深度学习模型的预测性能。决策曲线分析（DCA）用于评估新分期系统的临床实用性。 结果：建立了一个更适用和准确的深度学习模型，该模型在测试队列中的 OS 预测中优于传统的列线图（C-index：0.732[95%CI 0.714-0.750]与 0.671[95%CI 0.647-0.695]）。模型在测试队列中的 3 年和 5 年 OS 的 ROC 曲线也显示出良好的区分能力（3 年/5 年 OS 的曲线下面积[AUC]为 0.805/0.825）。此外，使用我们的新分期系统，我们观察到不同风险组之间的生存差异明显（P<0.001），并且在 DCA 中具有显著的净获益。 结论：为 ESCC 患者构建了一种新的基于深度学习的分期系统，该系统对生存概率具有显著的区分能力。此外，还实现了一个基于深度学习模型的易于使用的网络工具，为个性化生存预测提供了便利。我们开发了一种基于深度学习的系统，可根据患者的生存概率对 ESCC 患者进行分期。我们还创建了一个基于网络的工具，可使用该系统预测个体的生存结果。
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