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

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. MR images were obtained in 257 patients diagnosed with ALTs ( = 65) or lipomas ( = 192) using histology and the MD...

Full description

Saved in:

Bibliographic Details
Published in:	Cancers Vol. 15; no. 7; p. 2150
Main Authors:	Foreman, Sarah C, Llorián-Salvador, Oscar, David, Diana E, Rösner, Verena K N, Rischewski, Jon F, Feuerriegel, Georg C, Kramp, Daniel W, Luiken, Ina, Lohse, Ann-Kathrin, Kiefer, Jurij, Mogler, Carolin, Knebel, Carolin, Jung, Matthias, Andrade-Navarro, Miguel A, Rost, Burkhard, Combs, Stephanie E, Makowski, Marcus R, Woertler, Klaus, Peeken, Jan C, Gersing, Alexandra S
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 05-04-2023 MDPI
Subjects:	Connective tissue tumors Datasets Diagnosis Diagnosis, Differential Gene amplification Histology Learning algorithms Machine learning Magnetic resonance imaging MDM2 protein Methods Optimization Patients Radiogenetics Radiology Radiomics Reproducibility Sarcoma Tumors Germany radiomics soft-tissue sarcomas machine learning MRI radiology
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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. MR images were obtained in 257 patients diagnosed with ALTs ( = 65) or lipomas ( = 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. 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. 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work.
ISSN:	2072-6694 2072-6694
DOI:	10.3390/cancers15072150