ArticleQuantitative imaging in medicine and surgery2026
MRI analysis of undifferentiated pleomorphic sarcoma: correlating imaging features with histological grade.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Undifferentiated pleomorphic sarcoma (UPS) is an aggressive soft tissue malignancy in which the accurate assessment of histological grade is crucial for treatment planning and prognosis. Noninvasive magnetic resonance imaging (MRI)-based tumor features may reflect tumor biology, but their association with histological grade and survival outcomes in UPS remains unclear. The aim of this study was to investigate the relationship between MRI-derived tumor features, histological grade, and survival outcomes in patients with UPS, in order to evaluate the potential of MRI as a noninvasive tool for prognostication and treatment planning. Methods: This retrospective study included 83 patients with pathologically confirmed UPS between January 2015 and December 2023. All patients underwent pre-treatment 3.0 Tesla (3T) MRI scans, which included T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI). The MRI features assessed included growth pattern, signal intensity (SI) heterogeneity, necrosis volume, and apparent diffusion coefficient (ADC) values. Tumor histological grade was determined using the Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) system. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify imaging features associated with high-grade UPS. Results: The study included 83 patients with a mean age of 59.5 years, and 67.5% had high-grade (Grade II-III) tumors. Significant MRI features associated with high-grade UPS included diffuse infiltrative growth pattern (P=0.047), high T2-weighted SI heterogeneity (P=0.04), tumor volume with necrosis ≥50% (P=0.03), and lower ADC mean values (P=0.001). Multivariable analysis revealed that growth pattern, necrosis volume, and ADC difference were independent predictors of high-grade tumors. The combination of these features had a high diagnostic accuracy, with an area under the curve (AUC) of 0.876, sensitivity of 82.14%, and specificity of 85.19%. Conclusions: MRI features, including growth pattern, necrosis-related signal, and ADC values, are significantly associated with histological grade in UPS.
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