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Foreman, S.C.* ; Llorián-Salvador, O.* ; David, D.E.* ; Rösner, V.K.N.* ; Rischewski, J.F.* ; Feuerriegel, G.C.* ; Kramp, D.W.* ; Luiken, I.* ; Lohse, A.K.* ; Kiefer, J.* ; Mogler, C.* ; Knebel, C.* ; Jung, M.* ; Andrade-Navarro, M.A.* ; Rost, B.* ; Combs, S.* ; Makowski, M.R.* ; Woertler, K.* ; Peeken, J.C. ; Gersing, A.S.*

Development and Evaluation of MR-Based Radiogenomic Models to Differentiate Atypical Lipomatous Tumors from Lipomas.

Cancers 15:14 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
BACKGROUND: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. METHODS: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. RESULTS: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60-70% accuracy, 55-80% sensitivity, and 63-77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. CONCLUSION: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Mri ; Machine Learning ; Radiology ; Radiomics ; Soft-tissue Sarcomas; Soft-tissue Tumors; Liposarcoma; Diagnosis; Extremities; Mdm2; Classification; Amplification; Management; Features; Update
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2072-6694
Zeitschrift Cancers
Quellenangaben Band: 15, Heft: 7, Seiten: , Artikelnummer: 14 Supplement: ,
Verlag MDPI
Verlagsort St Alban-anlage 66, Ch-4052 Basel, Switzerland
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Radiation Sciences
PSP-Element(e) G-501300-001
Förderungen Clinician Scientist Program (KKF) at Technische Universitaet Muenchen (TUM)
Munich Clinician Scientist Program (MCSP) of the University of Munich (LMU)
European Society of Musculoskeletal Radiology (ESSR)
DGMSR)
German Society of Musculoskeletal Radiology (Deutsche Gesellschaft fur muskuloskelettale Radiologie
DOI 10.3390/cancers15072150
WOS ID 000969580100001
Scopus ID 85152449732
PubMed ID 37046811
Erfassungsdatum 2023-10-06
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