RT Journal Article SR Electronic T1 Magnetic Resonance Imaging Assessment of Lipomatous Soft-tissue Tumors JF In Vivo JO In Vivo FD International Institute of Anticancer Research SP 387 OP 395 VO 31 IS 3 A1 ALESSANDRO CORAN A1 PAOLO ORTOLAN A1 SHADY ATTAR A1 ENRICO ALBERIOLI A1 EGLE PERISSINOTTO A1 ANNA LISA TOSI A1 MARIA CRISTINA MONTESCO A1 CARLO RICCARDO ROSSI A1 SAVERIA TROPEA A1 MARCO RASTRELLI A1 ROBERTO STRAMARE YR 2017 UL http://iv.iiarjournals.org/content/31/3/387.abstract AB Aim: To establish the accuracy of magnetic resonance imaging (MRI) in distinguishing between benign and malignant lipomatous tumors; to evaluate the reproducibility of the MRI interpretation assessing the agreement between judgments of two radiologists with the same experience in soft-tissue sarcomas; to identify an association among MRI findings (size, depth, septa, nodules, signal homogeneity) and nature of the lesion. Materials and Methods: A total of 54 patients (28 men and 26 women), with a mean age of 56 (range=27-84) were included years. All subjects followed-up by the Multidisciplinary Sarcoma Group. The following MRI findings were judged in a blind study by two radiologists: size, localization, septa, nodules and signal homogeneity. A diagnostic indication was then given from among lipoma, atypical lipomatous tumour (ALT) and liposarcoma. Accuracy in distinguishing between benign and malignant lesions, and between lipoma and ALT (Fisher's exact test), inter-operator agreement (Cohen's kappa), association of MRI findings and malignancy of the lesion (Fisher's exact test and odds ratio) were evaluated. Results: The inter-operator agreement was complete (100%). The agreement between diagnostic hypothesis and histological diagnosis was statistically significant (p<0.05). Among the radiological findings taken into account, only septa and signal homogeneity were significantly associated with the malignancy of the lesion (p<0.05). Conclusion: MRI could be helpful in distinguishing lipomatous tumors, allowing biopsy to be avoided in some cases (negative predictive value=100%).