Advances in Radiation Oncology · 2019 · 74 citations · 34 references
MedicineOncologyRadiologyDiagnostic ImagingDiagnostic RadiologyOncologic ImagingMalignant NeoplasmsMalignant TumorsSoft Tissue SarcomasTumor CharacterizationTumor DetectionTumor ImagingOverall MortalitySarcoma ClassificationSarcoma GradingSarcoma StagingSoft Tissue Tumor Imaging
PURPOSE: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. METHODS AND MATERIALS: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell's concordance index. RESULTS: = .009). CONCLUSIONS: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS.
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Radiomics: Images Are More than Pictures, They Are Data
Robert J. Gillies, Paul E. Kinahan, Hedvig Hricak · Radiology · 2015 · 8.3K citations