Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events. Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables. We previously showed that predictive DL survival modeling of potentially curable stage I–III patients can be improved by combining time-fixed and time-varying covariates. Here, we demonstrate that DL-based predictive survival modeling in stage IV patients, where treatment does not offer a cure, can generate accurate individual survival predictions by considering subsequent lines of potential treatment to guide therapy. This guidance is rarely obtainable in the nearly limitless scenarios of metastatic disease. Design, Setting, and Participants: We applied the SEER-Medicare linked dataset from 1991 to 2016 to investigate 14,312 unique stage IV patients with 1,880,153 entries. We used DeepSurv- and DeepHit-, Nnet-survival- and Cox-Time DL-based predictive models to consider the combination of time-fixed and time-varying covariates at each visit for each patient. We adopted random sampling to divide the input dataset into training, validation, and testing sets. We verified the models’ implementation using the pycox package and fine-tuned the models using the open-source library Amazon SageMaker Python SDK 2.232.2 (software development kit). Our results demonstrated the proof of principle of the models by generating individual patients’ survival curves. Conclusions and Relevance: By extending the survival prediction models to consider stage IV BC patients’ time-fixed and time-varying covariates, we achieved a prediction error below 10%. Based on their circumstance-specific situations, these models can predict survival in individual stage IV patients with high confidence. The models will serve as an important adjunct to treatment decisions in patients with stage IV BC and test what-if scenarios of treatment or no treatment options to optimize therapy for extending patient lives and minimizing adverse events.
重要性：对于IV期乳腺癌（BC）女性的治疗，仅能将人群平均生存期延长数月。本研究旨在建立一个模型，用于识别在特定个体情况下，恰当的治疗能够延长生存期并同时最小化不良事件。目的：我们的目标是开发高置信度的深度学习（DL）模型，基于患者、癌症、治疗及不良事件变量所构成的独特情况，预测个体IV期乳腺癌患者的生存期。我们先前的研究表明，通过结合时间固定协变量与时间变化协变量，可改进对潜在可治愈的I–III期患者的预测性DL生存建模。在此，我们证明在无法通过治疗治愈的IV期患者中，基于DL的预测性生存建模能够通过考虑后续潜在治疗方案来指导治疗，从而生成准确的个体生存预测。这种指导在近乎无限的转移性疾病情境中通常难以获得。设计、设置与参与者：我们应用了1991年至2016年的SEER-Medicare关联数据集，研究了14,312名独特的IV期患者，共包含1,880,153条记录。我们使用基于DeepSurv、DeepHit、Nnet-survival和Cox-Time的DL预测模型，在每次就诊时综合考虑每位患者的时间固定协变量与时间变化协变量。采用随机抽样将输入数据集分为训练集、验证集和测试集。我们使用pycox包验证了模型的实现，并通过开源库Amazon SageMaker Python SDK 2.232.2（软件开发工具包）对模型进行了微调。通过生成个体患者的生存曲线，我们的结果验证了这些模型的原理可行性。结论与相关性：通过扩展生存预测模型以纳入IV期BC患者的时间固定协变量与时间变化协变量，我们实现了低于10%的预测误差。这些模型能够基于患者的具体情况，高置信度地预测个体IV期患者的生存期。这些模型将作为IV期BC患者治疗决策的重要辅助工具，并可用于测试治疗或不治疗等假设情景，以优化治疗方案，延长患者生命并最小化不良事件。
肿瘤(癌症)患者之家