Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report.
Ontology highlight
ABSTRACT: OBJECTIVES:Immune-checkpoint blockades have exhibited durable responses and improved long-term survival in a subset of advanced non-small cell lung cancer (NSCLC) patients. However, highly predictive markers of positive and negative responses to immunotherapy are a significant unmet clinical need. The objective of this study was to identify clinical and computational image-based predictors of rapid disease progression phenotypes in NSCLC patients treated with immune-checkpoint blockades. MATERIALS AND METHODS:Using time-to-progression (TTP) and/or tumor growth rates, rapid disease progression phenotypes were developed including hyperprogressive disease. The pre-treatment baseline predictors that were used to identify these phenotypes included patient demographics, clinical data, driver mutations, hematology data, and computational image-based features (radiomics) that were extracted from pre-treatment computed tomography scans. Synthetic Minority Oversampling Technique (SMOTE) was used to subsample minority groups to eliminate classification bias. Patient-level probabilities were calculated from the final clinical-radiomic models to subgroup patients by progression-free survival (PFS). RESULTS:Among 228 NSCLC patients treated with single agent or double agent immunotherapy, we identified parsimonious clinical-radiomic models with modest to high ability to predict rapid disease progression phenotypes with area under the receiver-operator characteristics ranging from 0.804 to 0.865. Patients who had TTP?
SUBMITTER: Tunali I
PROVIDER: S-EPMC6450086 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
ACCESS DATA