Abstract
To identify the magnetic resonance (MR) imaging features that can be used to differentiate high-grade from low-grade soft-tissue sarcoma (STS). Institutional review board approval was obtained, and informed consent was waived. Patients with STS who had undergone MR imaging with T1-weighted, T2-weighted, and contrast material-enhanced sequences prior to neoadjuvant therapy and surgery were included retrospectively. Tumor grade (grades 1-3) was recorded from the histologic specimen for each STS. Images were evaluated by two observers for tumor size and MR features (signal intensity, heterogeneity, margin, and perilesional characteristics) on images obtained with each sequence. Descriptive statistics for low-grade (grade 1) and high-grade (grades 2 and 3) STS were recorded, and the accuracy of individual features was determined. A multivariate logistic regression model was developed to identify features that were independently predictive of a high-grade tumor. Ninety-five patients (48 female [mean age, 55.8 years; age range, 7-96 years] and 47 male [mean age, 55.3 years; age range, 1-87 years]) with STS (16 patients with grade 1 STS, 34 patients with grade 2 STS, and 45 patients with grade 3 STS) were included. High-grade STS differed from low-grade STS in size (>5 cm, P = .004), tumor margin (partly or poorly defined margin on T1-weighted images, P = .002; with other sequences, P < .001), internal signal intensity composition (heterogeneous signal intensity on T2-weighted images, P = .009), and peritumoral characteristics (peritumoral high signal intensity on T2-weighted images, P = .025; peritumoral enhancement on contrast-enhanced T1-weighted images, P < .001). The logistic regression model showed that peritumoral contrast enhancement is the strongest independent indicator of high-grade status (odds ratio, 13.6; 95% confidence interval: 2.9, 64.6). Among several MR imaging features that aid in the discrimination of high-grade from low-grade sarcomas, the presence of peritumoral contrast enhancement is a feature that may be solely used to diagnose high-grade STS.
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