Department of Molecular Medicine and Surgery, Karolinska Institutet, Solnavägen 1, 171 77, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Solna, Stockholm, Sweden.
Department of Neuroradiology, Karolinska University Hospital, Solna, Stockholm, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Clin Radiol. 2019 Jul;74(7):534-538. doi: 10.1016/j.crad.2019.01.011. Epub 2019 Apr 15.
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 acid composition (FAC) and three-dimensional (3D) texture analysis was tested.
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.
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).
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.
评估定量质子密度脂肪分数(PDFF)是否有助于区分四肢和躯干的脂肪瘤、非典型性脂肪肉瘤(ALT)和脂肪肉瘤。此外,还测试了基于磁共振成像(MRI)的脂肪酸组成(FAC)和三维(3D)纹理分析在区分 ALT 与非典型性脂肪瘤中的作用。
本前瞻性研究(2014-2017 年进行;包括 20 名女性,21 名男性)获得了区域伦理审查委员会的批准,并获得了所有参与者的知情同意。为了获取 PDFF 和 FAC 的 3D 扰相梯度多回波图像,我们对 16 个脂肪瘤(25-76 岁)、14 个 ALT(42-78 岁)和 11 个黏液样脂肪肉瘤(31-68 岁)进行了分析。采用单因素方差分析比较平均 PDFF 的差异。使用支持向量机算法寻找分离平均 PDFF 值的方法。
脂肪瘤的平均 PDFF 为 90%(范围 76-98%),ALT 为 83%(范围 62-91%),脂肪肉瘤为 4%(范围 0-21%)。脂肪肉瘤与 ALT 和脂肪瘤之间的平均 PDFF 差异有统计学意义(p=0.0001,均),而 ALT 与脂肪瘤之间的差异也有统计学意义(p=0.021)。将脂肪肉瘤与 ALT 和脂肪瘤分离的最佳阈值为 41.5%,将 ALT 和脂肪瘤分离的最佳阈值为 85%。纹理分析无法分离 ALT 和非典型性脂肪瘤,而 FAC 不饱和程度的差异具有统计学意义(p=0.013)。
测量 PDFF 是对标准 MRI 的有前景的补充,可以将脂肪肉瘤与 ALT 和脂肪瘤区分开来。不能仅通过 PDFF、FAC 或纹理分析将非纯脂肪性脂肪瘤与 ALT 区分开来。