Evaluation of Classic and Quantitative Imaging Features in the Differentiation of Benign and Atypical Lipomatous Soft Tissue Tumors Using a Standardized Multiparametric MRI Protocol: A Prospective Single-Centre Study in 45 Patients.

BACKGROUND: Discrimination between benign and atypical lipomatous tumors (ALT) is important due to potential local complications and recurrence of ALT but can be difficult due to the often-similar imaging appearance. Using a standardized MRI protocol, this study aimed to rank established and quantitative MRI features by diagnostic value in the differentiation of benign and atypical lipomatous tumors and to develop a robust scoring system. METHODS: Patients with clinical or sonographic suspicion of a lipomatous tumor were prospectively and consecutively enrolled from 2015 to 2019 after ethic review board approval. Histology was confirmed for all ALT and 85% of the benign cases. Twenty-one demographic and morphologic and twenty-three quantitative features were extracted from a standardized MRI protocol (T1/T2-proton-density-weighting, turbo-inversion recovery magnitude, T2* multi-echo gradient-echo imaging, qDIXON-Vibe fat-quantification, T1 relaxometry, T1 mapping, diffusion-weighted and post-contrast sequences). A ranking of these features was generated through a Bayes network analysis with gain-ratio feature evaluation. RESULTS: Forty-five patients were included in the analysis (mean age, 61.2 ± 14.2 years, 27 women [60.0%]). The highest-ranked ALT predictors were septation thickness (gain ratio merit [GRM] 0.623 ± 0.025, p = 0.0055), intra- and peritumoral STIR signal discrepancy (GRM 0.458 ± 0.046, p < 0.0001), orthogonal diameter (GRM 0.554 ± 0.188, p = 0.0013), contrast enhancement (GRM 0.235 ± 0.015, p = 0.0010) and maximum diameter (GRM 0.221 ± 0.075, p = 0.0009). The quantitative features did not provide a significant discriminatory value. The highest-ranked predictors were used to generate a five-tiered score for the identification of ALTs (correct classification rate 95.7% at a cut-off of three positive items, sensitivity 100.0%, specificity 94.9%, likelihood ratio 19.5). CONCLUSIONS: Several single MRI features have a substantial diagnostic value in the identification of ALT, yet a multiparametric approach by a simple combination algorithm may support radiologists in the identification of lipomatous tumors in need for further histological assessment.

MRI of middle ear cholesteatoma: The importance of observer reliance from diffusion sequences.

BACKGROUND AND PURPOSE: Diffusion-weighted imaging(DWI) in MRI has been developed as an important tool for the detection of cholesteatoma. Various DWI sequences are available. This study aims to evaluate the importance of the observer's reliance level for the detection of cholesteatoma. METHODS: Forty patients meeting the following criteria were included in the study: (1) chronic otitis media, (2) preoperative MRI including various DWI sequences, and (3) middle-ear surgery. The MRI protocol contained the following sequences: (1) axial and (2) coronal echoplanar imaging (EPI) readout-segmented (RESOLVE) DWI with Trace acquisition and (3) coronal non-EPI half-Fourier acquired single-shot turbo spin-echo (HASTE) DWI. Cholesteatoma diagnosis was based on standard diagnostic criteria for cholesteatoma with DWI. Additionally, the radiologists were asked to grade personal reliance on their diagnosis using a Likert-type scale from 1 = very insecure to 5 = very secure. RESULTS: Axial and coronal RESOLVE DWI showed a sensitivity of 77.3% and a specificity of 72.2%, respectively. The mean reliance was 3.9 for axial and 3.8 for coronal images. HASTE DWI had a sensitivity/specificity of 81.8%/66.7% with the highest reliance of all evaluated sequences (4.4). Cases with a reliance level of 5 showed a sensitivity/specificity of 100% in all sequences. A reliance level of 5 was given in the axial and coronal RESOLVE DWI in 32.5% of cases and in the HASTE DWI in 57.5%. CONCLUSION: The evaluated DWI sequences showed comparable results. The reliance level significantly improved the predictor of cholesteatoma disease with MRI techniques.