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Peeken, J.C. ; Spraker, M.B.* ; Knebel, C.* ; Dapper, H.* ; Pfeiffer, D.* ; Devecka, M.* ; Thamer, A.* ; Shouman, M.A.* ; Ott, A.* ; von Eisenhart-Rothe, R.* ; Nüsslin, F.* ; Mayr, N.A.* ; Nyflot, M.J.* ; Combs, S.E.

Tumor grading of soft tissue sarcomas using MRI-based radiomics.

EBioMedicine 48, 332-340 (2019)
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Open Access Gold
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Background: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS.Methods: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects.Findings: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone.Interpretation: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. (C) 2019 The Authors. Published by Elsevier B.V.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Soft Tissue Sarcoma ; Radiomics ; Tumor Grading ; Mri ; Risk Stratification ; Biomarker; Prediction; Model
Language
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 2352-3964
e-ISSN 2352-3964
Journal EBioMedicine
Quellenangaben Volume: 48, Issue: , Pages: 332-340 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam [u.a.]
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)
POF-Topic(s) 30203 - Molecular Targets and Therapies
Research field(s) Radiation Sciences
PSP Element(s) G-501300-001
DOI 10.1016/j.ebiom.2019.08.059
WOS ID WOS:000493830800037
Scopus ID 85072051854
Erfassungsdatum 2019-10-02
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