Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning

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dc.authoridBatur, Halitcan/0000-0002-4743-7926
dc.authoridMendi, Bokebatur Ahmet Rasit/0000-0002-6102-2188
dc.contributor.authorCay, Nurdan
dc.contributor.authorMendi, Bokebatur Ahmet Rasit
dc.contributor.authorBatur, Halitcan
dc.contributor.authorErdogan, Fazli
dc.date.accessioned2024-11-07T13:32:55Z
dc.date.available2024-11-07T13:32:55Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractPurpose To evaluate the diagnostic capability of radiomics in distinguishing lipoma and Atypic Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDL) with Magnetic Resonance Imaging (MRI). Materials and methods Patients with a histopathologic diagnosis of lipoma (n = 45) and ALT/WDL (n = 20), who had undergone pre-surgery or pre-biopsy MRI, were enrolled. The MDM2 amplification was accepted as gold-standard test. The T1-weighted turbo spin echo images were used for radiomics analysis. Utility of a predefined standardized imaging protocol and a single type of 1.5 T scanner were sought as inclusion criteria. Radiomics parameters that show a certain level of reproducibility were included in the study and supplied to Support Vector Machine (SVM) as a machine learning method. Results No significant difference was found in terms of gender, location and age between the lipoma and ALT/WDL groups. Sixty-five parameters were accepted as reproducible. Fifty-seven parameters were able to distinguish the two groups significantly (AUC range 0.564-0.902). Diagnostic performance of the SVM was one of the highest among literature findings: sensitivity = 96.8% (95% CI 94.03-98.39%), specificity = 93.72% (95% CI 86.36-97.73%) and AUC = 0.987 (95% CI 0.972-0.999). Conclusion Although radiomics has been proven to be useful in previous literature regarding discrimination of lipomas and ALT/WDLs, we found that its accuracy could further be improved with utility of standardized hardware, imaging protocols and incorporation of machine learning methods.
dc.identifier.doi10.1007/s11604-022-01278-x
dc.identifier.endpage960
dc.identifier.issn1867-1071
dc.identifier.issn1867-108X
dc.identifier.issue9
dc.identifier.pmid35430677
dc.identifier.scopus2-s2.0-85128237076
dc.identifier.scopusqualityQ2
dc.identifier.startpage951
dc.identifier.urihttps://doi.org/10.1007/s11604-022-01278-x
dc.identifier.urihttps://hdl.handle.net/11480/15672
dc.identifier.volume40
dc.identifier.wosWOS:000783032500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJapanese Journal of Radiology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectAtypical lipomatous tumor
dc.subjectLipoma
dc.subjectMachine learning
dc.subjectMRI
dc.subjectRadiomics
dc.subjectWell-differentiated liposarcoma
dc.titleDiscrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning
dc.typeArticle

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