Multimodal imaging findings including high-resolution 3D T2-weighted imaging for COVID-19 vaccine-associated axillary lymphadenopathy in a patient with breast cancer.

COVID-19 vaccines have received authorization worldwide. Vaccines are typically administered to the deltoid muscle, and axillary swelling/tenderness at the first dose (11.6%) and the second dose (16%) have been reported as secondary effects. Regional lymphadenopathy in the axilla and supraclavicular region has also been reported with a prevalence of 1.1% and is referred to as COVID-19 vaccine-associated lymphadenopathy (VAL). COVID-19 VAL mimics lymph node (LN) metastases on magnetic resonance imaging, ultrasound, and 18F-fluoro-2-deoxy-Dglucose positron emission tomography. Although several specific findings of VAL on clinical imaging have been reported, the difficulty in differentiating between VAL and LN metastases could lead to false-positive or -negative diagnoses. Here, we report a case of breast cancer with ipsilateral VAL with multimodal imaging including 3D T2-weighted imaging, a new magnetic resonance imaging technique, and discuss the future perspective for differentiating between VAL and LN metastases.

Apparent diffusion coefficient and intravoxel incoherent motion-diffusion kurtosis model parameters in invasive breast cancer: Correlation with the histological parameters of whole-slide imaging.

OBJECTIVES: To evaluate the correlation of ADC and IVIM-diffusion kurtosis (DK) model parameters with quantitative histological parameters of whole-slide imaging (WSI). METHODS: This retrospective study (September 2015-July 2016) included 37 consecutive patients (all females; median age: 64 years, range, 41-82 years), each with a single invasive breast ductal carcinoma lesion with a mass appearance on preoperative MRI. DWI with b-values of 0, 50, 100, 300, 550, 850, and 1000 s/mm2 was performed. ADC maps were generated with b-values of 50 and 850 s/mm2. The IVIM-DK model was analysed using the following formula: [Formula: see text] where S is the signal intensity, b is the b-value, f is the perfusion fraction, D* is the pseudo-diffusion coefficient of the vascular component, D is the diffusion coefficient of the non-vascular component, and k is the diffusion kurtosis. Whole tumour segmentation was performed to obtain the mean ADC, f, D*, D, and k. Quantitative histological parameters were obtained using cytokeratin immunostaining and WSI. The correlation of ADC and IVIM-DK model parameters with quantitative histological parameters was examined by Pearson's correlation. RESULTS: The ADC was significantly correlated with the area ratio of interstitium (r = 0.53, p = 0.00082) and entropy (r = -0.58, p = 0.00019). k was significantly correlated with the area ratio of cancer cell nuclei (r = 0.53, p = 0.00079). CONCLUSIONS: Since the ADC reflected the area ratio of interstitium and entropy, and diffusion kurtosis reflected the area ratio of cancer cell nuclei, these parameters may be effective in distinguishing between benign and malignant breast tumours and in grading breast cancer.

American College of Rheumatology and the European League Against Rheumatism classification criteria for IgG4-related disease: an update for radiologists.

IgG4-related disease (IgG4-RD) is an immune-mediated, multiorgan, chronic inflammatory disease. The three-step classification criteria proposed in 2019 by the American College of Rheumatology and the European League Against Rheumatism (ACR/EULAR) encompass a wide range of clinical, radiological, serological, and histopathological findings. The ACR/EULAR proposed a three-step classification process, i.e., entry step, exclusion step, and scoring system. Radiologists need to know that the radiological findings observed in the five domains of the lacrimal and salivary glands, chest, pancreas and biliary ducts, kidney, and retroperitoneum are independently weighted with different points in the scoring system. A total score < 20 points indicates that the patient should not be classified as having IgG4-RD; conversely, a total score ≥ 20 points indicates that the patient should be classified as having IgG4-RD. In this review, the 2019 ACR/EULAR classification criteria are discussed, focusing on the interpretation of each radiological item, with the aim of applying them to the diagnosis of IgG4-RD in clinical practice.

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.

Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging.

PURPOSE: To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS). METHODS: Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance. RESULTS: The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001). CONCLUSION: The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.

Effective apparent diffusion coefficient parameters for differentiation between mass-forming autoimmune pancreatitis and pancreatic ductal adenocarcinoma.

PURPOSE: To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) parameters by region of interest (ROI) methods in differentiating mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). METHODS: The institutional review board approved this retrospective study and the requirement for informed consent was waived. Twenty-three patients with mass-forming AIP and 144 patients with PDAC underwent diffusion-weighted imaging with b-values of 0 s/mm2 and 800 s/mm2. The minimum, maximum, and mean ADC values obtained by placing ROIs within lesions and percentile ADC values (10th, 25th, 50th, 75th, and 90th) from entire-lesion histogram analysis were compared between the two groups by using Mann-Whitney U tests. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: The minimum, maximum, and mean ADC values were significantly different between mass-forming AIP and PDAC groups. ROC curve analysis showed that the maximum ADC had the highest diagnostic performance (0.92), while the minimum ADC value had the lowest diagnostic performance (0.72). The AUC of minimum ADC was significantly lower than that of maximum or mean ADC (P < 0.0001, P < 0.0001). The AUC was lowest in 10th percentile ADC value and highest in 90th percentile value. The AUC increased along with the increase of percentile values. CONCLUSION: Either the maximum or mean ADC value was effective in differentiating mass-forming AIP from the PDAC group, while the minimum ADC value might not be recommended.