Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional survival analysis techniques such as Cox proportional hazards (CPH) and recursive partitioning analysis (RPA). Previous studies have shown that by evaluating CPH risk with a nonlinear deep neural network, DeepSurv, patient survival can be modeled more accurately. In this work, we apply DeepSurv to a test case: breast cancer patients with brain metastases who have received stereotactic radiosurgery.

中文翻译：

---

基于国家癌症数据库的脑转移患者的深度学习生存分析。

预后指标，如脑转移分级预后评估，已在临床环境中使用，以帮助医生和患者确定合适的治疗方案。这些指数是传统生存分析技术的衍生物，例如 Cox 比例风险 (CPH) 和递归分区分析 (RPA)。先前的研究表明，通过使用非线性深度神经网络 DeepSurv 评估 CPH 风险，可以更准确地为患者生存建模。在这项工作中，我们将 DeepSurv 应用于测试案例：接受立体定向放射外科手术的脑转移乳腺癌患者。