Abstract
The growing amount of longitudinal data for a large population of patients has necessitated the application of algorithms that can discover patterns to inform patient management. This study demonstrates how temporal patterns generated from a combination of clinical and imaging measurements improve residual survival prediction in glioblastoma patients. Temporal patterns were identified with sequential pattern mining using data from 304 patients. Along with patient covariates, the patterns were incorporated as features in logistic regression models to predict 2-, 6-, or 9-month residual survival at each visit. The modeling approach that included temporal patterns achieved test performances of 0.820, 0.785, and 0.783 area under the receiver operating characteristic curve for predicting 2-, 6-, and 9-month residual survival, respectively. This approach significantly outperformed models that used tumor volume alone (p < 0.001) or tumor volume combined with patient covariates (p < 0.001) in training. Temporal patterns involving an increase in tumor volume above 122 mm3/day, a decrease in KPS across multiple visits, moderate neurologic symptoms, and worsening overall neurologic function suggested lower residual survival. These patterns are readily interpretable and found to be consistent with known prognostic indicators, suggesting they can provide early indicators to clinicians of changes in patient state and inform management decisions.
Highlights
While studies have identified prognostic clinical, imaging, and molecular traits[5,6,7,8], they are typically analyzed at a single time point or based on observations not routinely available in the clinic
An unprecedented amount of clinical information can be captured by clinicians for research purposes and by electronic medical records
Clinicians are presented with the challenge of interpreting this data and identifying trends to inform individualized decision-making
Summary
While studies have identified prognostic clinical, imaging, and molecular traits[5,6,7,8], they are typically analyzed at a single time point or based on observations not routinely available in the clinic (e.g., multimodal genomic data). Other investigators used sequential pattern mining to identify treatment pathways in GBM patients that were predictive of one-year overall survival[16] using data from The Cancer Genome Atlas (TCGA); they focused on drug treatment patterns and multimodal molecular traits. We developed and evaluated a method to mine longitudinal patterns, termed temporal patterns, from clinical and imaging data to determine residual survival at an individual patient encounter. Residual survival is defined as the remaining number of days until death for a patient at a given clinical visit. We applied machine learning to select temporal patterns that were predictive of 2-, 6-, or 9-month residual survival. Our objective was to eventually develop a decision support tool that aids clinicians when assessing patients to identify patterns that predict death
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