FDG PET/CT and MRI Findings of Extraskeletal Myxoid Chondrosarcoma Showing a Nonmyxoid Cellular Variant.

ABSTRACT: We present a new, extremely rare nonmyxoid cellular variant of extraskeletal myxoid chondrosarcoma. Although diagnosis is radiologically and pathologically challenging, FDG PET/CT and MRI accurately showed the malignancy and high tumor density. A 52-year-old woman complained of a left dorsal mass, which presented inhomogeneous intermediate signals on T2-weighted images, with diffusion restriction, strong enhancement, and increased accumulation of FDG (SUV max , 5.2). Although biopsy was inconclusive, a highly malignant tumor was suspected radiologically. The resected specimen was histologically diagnosed as extraskeletal myxoid chondrosarcoma by detection of EWSR1::NR4A3 fusion using fluorescence in situ hybridization.

Abnormal Findings on "T1WI or DWI or MRCP:" An Effective Boolean Interpretation Model in Discriminating Small Pancreatic Ductal Adenocarcinoma from Control Group.

OBJECTIVES: The objectives of the study was to evaluate the diagnostic performance of findings on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and magnetic resonance cholangiopancreatography (MRCP) separately and to identify an optimal Boolean interpretation model for discriminating patients with small pancreatic ductal adenocarcinoma (PDAC) from control groups in clinical practice. MATERIAL AND METHODS: We retrospectively enrolled 30 patients with surgery confirmed small PDAC (≤20 mm) and 302 patients without pancreatic abnormality between April 2008 and February 2020. The presence of masses was evaluated by T1WI, T2WI, and DWI. Abnormality of the main pancreatic duct (MPD) was evaluated by T2WI and MRCP. Multivariate logistic regression analysis was performed to select significant sequences for discriminating the small PDAC and control groups. Boolean operators "OR" or "AND" were used to construct sequence combinations. Diagnostic performances of these sequences and combinations were evaluated by X 2 tests. RESULTS: The sensitivity of T2WI was lowest (20%) for detecting masses. For evaluating MPD abnormality, sensitivity was higher for MRCP than for T2WI (86.7% vs. 53.3%). Multivariate logistic regression analysis showed that T1WI and DWI for detecting the presence of masses and MRCP for evaluating MPD abnormality were significantly associated with differentiation between the two groups (P = 0.0002, P = 0.0484, and P < 0.0001, respectively). Seven combinations were constructed with T1WI, DWI, and MRCP. The combination of findings on "T1WI or DWI or MRCP" achieved the highest sensitivity of 96.7% and negative predictive value of 99.6%. CONCLUSION: The combination of findings on "T1WI or DWI or MRCP" might be an optimal interpretation model for discriminating small PDAC from control groups in clinical practice.

Discriminating low-grade ductal carcinoma in situ (DCIS) from non-low-grade DCIS or DCIS upgraded to invasive carcinoma: effective texture features on ultrafast dynamic contrast-enhanced magnetic resonance imaging.

PURPOSE: To investigate effective model composed of features from ultrafast dynamic contrast-enhanced magnetic resonance imaging (UF-MRI) for distinguishing low- from non-low-grade ductal carcinoma in situ (DCIS) lesions or DCIS lesions upgraded to invasive carcinoma (upgrade DCIS lesions) among lesions diagnosed as DCIS on pre-operative biopsy. MATERIALS AND METHODS: Eighty-six consecutive women with 86 DCIS lesions diagnosed by biopsy underwent UF-MRI including pre- and 18 post-contrast ultrafast scans (temporal resolution of 3 s/phase). The last phase of UF-MRI was used to perform 3D segmentation. The time point at 6 s after the aorta started to enhance was used to obtain subtracted images. From the 3D segmentation and subtracted images, enhancement, shape, and texture features were calculated and compared between low- and non-low-grade or upgrade DCIS lesions using univariate analysis. Feature selection by least absolute shrinkage and selection operator (LASSO) algorithm and k-fold cross-validation were performed to evaluate the diagnostic performance. RESULTS: Surgical specimens revealed 16 low-grade DCIS lesions, 37 non-low-grade lesions and 33 upgrade DCIS lesions. In univariate analysis, five shape and seven texture features were significantly different between low- and non-low-grade lesions or upgrade DCIS lesions, whereas enhancement features were not. The six features including surface/volume ratio, irregularity, diff variance, uniformity, sum average, and variance were selected using LASSO algorism and the mean area under the receiver operating characteristic curve for training and validation folds were 0.88 and 0.88, respectively. CONCLUSION: The model with shape and texture features of UF-MRI could effectively distinguish low- from non-low-grade or upgrade DCIS lesions.