MRI radiomic features are independently associated with overall survival in soft tissue sarcoma

Authors

Matthew B Spraker
Landon S Wootton
Daniel S Hippe
Kevin C. Ball, Advocate Aurora HealthFollow
Jan C Peeken
Meghan W Macomber
Tobias R Chapman
Michael N Hoff
Edward Y Kim
Seth M Pollack
Stephanie E Combs
Matthew J Nyflot

Recommended Citation

Spraker MB, Wootton LS, Hippe DS, et al. MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma. Adv Radiat Oncol. 2019;4(2):413-421. doi: 10.1016/j.adro.2019.02.003

Affiliations

Aurora St. Luke's Medical Center, Department of Diagnostic Radiology

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: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4;

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.

Document Type

Article

PubMed ID

31011687