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Peeken, J.C. ; Neumann, J.* ; Asadpour, R.* ; Leonhardt, Y.* ; Moreira, J.R.* ; Hippe, D.S.* ; Klymenko, O.* ; Foreman, S.C.* ; von Schacky, C.E.* ; Spraker, M.B.* ; Schaub, S.K.* ; Dapper, H.* ; Knebel, C.* ; Mayr, N.A.* ; Woodruff, H.C.* ; Lambin, P.* ; Nyflot, M.J.* ; Gersing, A.S.* ; Combs, S.E.

Prognostic assessment in high-grade soft-tissue sarcoma patients: A comparison of semantic image analysis and radiomics.

Cancers 13:1929 (2021)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fatsaturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Elastic Net Regression ; Machine Learning ; Mri ; Prognosis ; Radiology ; Radiomics ; Soft-tissue Sarcomas ; Tail Sign
ISSN (print) / ISBN 2072-6694
Journal Cancers
Quellenangaben Volume: 13, Issue: 8, Pages: , Article Number: 1929 Supplement: ,
Publisher MDPI
Publishing Place St Alban-anlage 66, Ch-4052 Basel, Switzerland
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)
Grants Technische Universität München
Helmholtz Zentrum Munchen