MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma

Matthew B. Spraker, Landon S. Wootton, Daniel S. Hippe, Kevin C. Ball, Jan C. Peeken, Meghan W. Macomber, Tobias R. Chapman, Michael N. Hoff, Edward Y. Kim, Seth M. Pollack,

Advances in Radiation Oncology · 2019 · 74 citations · 34 references

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Abstract

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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