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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)
Verlagsversion Forschungsdaten 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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Elastic Net Regression ; Machine Learning ; Mri ; Prognosis ; Radiology ; Radiomics ; Soft-tissue Sarcomas ; Tail Sign
ISSN (print) / ISBN 2072-6694
Zeitschrift Cancers
Quellenangaben Band: 13, Heft: 8, Seiten: , Artikelnummer: 1929 Supplement: ,
Verlag MDPI
Verlagsort St Alban-anlage 66, Ch-4052 Basel, Switzerland
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
Förderungen Technische Universität München
Helmholtz Zentrum Munchen