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. 1997;1(3-4):175-9.
doi: 10.1080/13577149778272.

Histologic correlation with magnetic resonance imaging for benign and malignant lipomatous masses

Affiliations
Free PMC article

Histologic correlation with magnetic resonance imaging for benign and malignant lipomatous masses

B T Rougraff et al. Sarcoma. 1997.
Free PMC article

Abstract

Purpose/results. We evaluated the diagnostic accuracy of magnetic resonance imaging (MRI) for 46 consecutive patients with lipomatous soft tissue tumors prior to biopsy and resection. Twenty-eight patients had benign lipomas and 18 had liposarcomas. Clinical differences between thdse patients with benign disease and those with malignant lesions were average age at the time of presentation (49 years for benign vs 62 years for malignant, p < 0.001) and average length of symptoms prior to resection (64 months for benign versus 38 months for malignant, p = 0.01). MRI characteristics associated with benign disease included: smaller tumor size (9.4 cm average greatest dimension for benign lesions vs 13.4 cm for malignant masses, p = 0.022); a mass with a uniformly homogeneous signal (p = 0.0003); a mass with homogeneous high T1 and T2 signals and a low short-time-inversion-recovery (STIR) signal comparable to normal fat (p < 0.0001). This last signal pattern was not seen in malignant lesions (0/18) and was present in almost all benign lipomas (25/28). The usual MRI descriptions of soft tissue masses such as infiltrating vs encapsulating, deep vs subcutaneous and septated vs non-septated were not helpful predictors of malignancy in this series. Needle biopsies of lipomatous masses with heterogeneous signals on MRI resulted in inaccurate diagnoses due to sampling error in 5/9 patients.Discussion. A carefully planned and performed MRI study of lipomatous masses can accurately predict a benign lipoma whenever a homogeneous high T1 and T2, as well as a low STIR, signal is present. However, a mass with any other signal characteristics must be biopsied carefully in order to make an accurate diagnosis.

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