Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.

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.