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

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; P = .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.

Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients

“…Our results also deepened that intra-tumoral heterogeneous SIs on T2-WI is predictive of MFS in a quantitative manner and other studies have also correlated this parameter with overall and/or metastatic-relapse free survivals in STS patients with relatively close and similar performances to ours 6,7,20 . Indeed, Peeken et al used an equivalent of IHTstd and applied ComBat to correct for multicenter effect.…”

“…In this case, lowering these performances with a particular IHT would mean that this IHT caused noise and inappropriate deviation in the data. However, as already stated, prior studies converged towards same results regarding the relationship between MRI-based radiomics features, heterogeneity on T2-WI and outcomes of sarcoma patients 6,7,20,35 . Alternative study designs could have been proposed in the absence of such relationship, (i) either by using a phantom made of compartments with various degrees of heterogeneity, (ii) or by using MRIs of healthy volunteers covering organs with different textures and investigating which IHT enables the best radiomics-based classification of these organs (by analogy with the study by Orlhac et al) 19 .…”

“…Their best model relied on radiomics features from Fat Sat T2 weighted imaging and showed a concordance-index of 0.74 in the validation cohort. On the other hand, Spraker et al did not explicitly use an intensity harmonization technique, neither ComBat 7 . Interestingly, their best clinical and radiological prognostic models for the overall survival showed a concordance-index of 0.78 in the validation cohort.…”

MRI imaging of soft tissue tumours of the foot and ankle

“…It should be kept in mind that considerable overlap between benign and malignant lesions has been found with all of these techniques. Radiomic analysis of MR images has been shown to have some prognostic value in soft tissue sarcoma but is not yet a mature technique [7, 8].…”

Magnetic Resonance Imaging-based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-center Study

“…Spraker et al [20] found that radiomics features alone or combined with age and grade were respectively independent predictors of over survival, and the latter achieved best performance. Peeken et al [37] demonstrated that the radiomics model based on T1FSGd MRI sequences showed signi cant patient strati cation performance for overall survival, and improved prognostic performance was found with the combination of a T2FS radiomics model with the AJCC system.…”