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Published on: July 19, 2019
Magnetic resonance imaging assessing the correlation of components and prognosis in myxoid liposarcoma
Jianjun Hua1, Wenting Yang1, Angcheng Li2
1Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, PR China.
Abstract:
BackgroundMyxoid liposarcoma (MLS) is a subtype of liposarcoma characterized by its myxoid stroma and adipocyte differentiation. MLS is prone to recurrence and metastasis. Magnetic resonance imaging (MRI) plays a crucial role in evaluating tumor characteristics, enabling accurate diagnosis, and predicting patient prognosis.PurposeTo analyze the components of MLS by MRI features and assess their correlation with prognosis.Material and MethodsA total of 20 patients with MLS who underwent MRI were retrospectively included. Tumor components were analyzed by MRI features, and their prognostic correlation was assessed. Patients were divided into good and poor prognosis groups based on postoperative follow-up.ResultsThe proportions of non-fatty/non-myxoid components in the good and poor prognosis groups were 15.00% (range = 10.00%-20.00%) and 70.00% (range = 52.50%-77.50%), respectively (P < 0.001). The proportion of myxoid composition also differed significantly between the two groups (75.00%, [range = 65.00%-85.00%] vs. 25.00% [range = 17.50%-42.50%]; P < 0.001). The good prognosis group had a greater mean apparent diffusion coefficient (ADC) value (1.66 ± 0.23 × 10-3 mm2/s) and a lower mean ADC low signal ratio (5.00% [range = 0%-10.00%]) in the non-fatty/non-myxoid areas than the poor group (1.21 ± 0.41 × 10-3 mm2/s; 20.00% [range = 11.00%-39.00%]; P = 0.006 and P = 0.001). The differences in the percentages of patients with a component ratio <25% and >50% in both the non-fatty/non-myxoid and myxoid groups were significant (P < 0.001 and P = 0.005).ConclusionImaging features were closely associated with the histological components of MLS. The use of MRI features for assessing MLS components has important implications for prognostic prediction.
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