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Soft-tissue fat tumours: differentiating malignant from benign using proton density fat fraction quantification MRI
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Clinical Radiology, ISSN 0009-9260, E-ISSN 1365-229X, Vol. 74, nr 7, s. 534-538Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Aim: To evaluate if quantifying proton density fat fraction (PDFF) would be useful in separating lipoma, atypical lipomatous tumour (ALT) and liposarcoma in the extremities and trunk. In addition, differentiating ALT versus non-classical lipomas using magnetic resonance imaging (MRI)-based fatty acidcomposition (FAC) and three-dimensional (3D) texture analysis was tested.

Material and methods: This prospective study (undertaken between 2014–2017; comprising 20 women, 21 men) was approved by the Regional Ethical Review Board and informed consent was obtained from all participants. For PDFF and FAC 3D spoiled gradient multi-echo images were acquired. PDFF was analysed in 16 lipomas (25–76 years), 14 ALTs (42–78 years) and 11 myxoid liposarcomas (31–68 years). The difference of mean PDFF was tested with one-way analysis of variance. A support vector machine algorithm was used to find the separating mean PDFF values.

Results: Mean PDFF for lipomas was 90% (range 76–98%), for ALT 83% (range 62–91%), and for liposarcoma 4% (range 0–21%). The difference of mean PDFF for liposarcomas versus ALT and lipoma was significant (p=0.0001, for both), and for ALT versus lipoma (p=0.021). The optimal threshold for separating liposarcoma from ALT and lipoma was 41.5%, and for ALT and lipoma 85%. Texture analysis could not separate ALT and non-classical lipomas, while the difference for FAC unsaturation degree was significant (p=0.013).

Conclusion: Measuring PDFF is a promising complement to standard MRI, to separate liposarcomas from ALT and lipomas. Lipomas that are not solely composed of fat cannot confidently be separated from ALT using PDFF, FAC, or texture analysis.

sted, utgiver, år, opplag, sider
Saunders Elsevier, 2019. Vol. 74, nr 7, s. 534-538
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-160276DOI: 10.1016/j.crad.2019.01.011ISI: 000469026600007PubMedID: 31000331OAI: oai:DiVA.org:umu-160276DiVA, id: diva2:1326092
Tilgjengelig fra: 2019-06-17 Laget: 2019-06-17 Sist oppdatert: 2019-06-17bibliografisk kontrollert

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