Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients.

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-

SUBMITTER: Smedley NF 

PROVIDER: S-EPMC6158293 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature