Differentiating gallbladder cancer from polyps using non-enhanced magnetic resonance imaging.

Purpose: This retrospective cohort study assessed the efficiency of non-enhanced magnetic resonance imaging (MRI) for differentiating gallbladder cancer (GBC) from gallbladder polyps (GBPs) measuring ≥ 10 mm. Material and methods: Patients diagnosed with GBCs or GBPs ≥ 10 mm and GBC ≤ T2 stage were eligible for inclusion. Two independent blinded readers assessed the continuity of the mucosal and muscular layers (CMML; present or absent) and normalised signal intensity ratio (NIR) on the apparent diffusion coefficient map (NIR-ADC), T1-weighted image (NIR-T1WI), and T2-weighted half-Fourier acquisition single-shot turbo spin-echo image. Univariate and multivariate logistic regression analyses and interobserver agreement analyses were performed to detect predictive variables differentiating GBCs from GBPs. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance. A reproducibility test was performed to verify the predictive variables. Results: Multivariate analysis showed significant differences in CMML, NIR-ADC, and NIR-T1WI (p < 0.001). The positive predictive value (PPV) and specificity of the absence of CMML were approximately 100%. The CMML showed the best specificity, accuracy, and PPV in the reproducibility study. The sensitivity of CMML alone was approximately 50%, whereas it increased to approximately 70% when combined with NIR-ADC. The diagnostic performance of the combination, including sensitivity, was almost like that of tumour size. The combined tumour size and CMML assessment showed higher diagnostic performance than tumour size alone. Conclusions: The absence of CMML and NIR-ADC ≤ 1.86 helped in differentiating GBCs from GBPs. Evaluation of the absence of CMML and measurement of tumour size could better aid in determining between the two than measurement of tumour size alone.

Evaluation of the association between osteoporotic vertebral compression fractures and psoas major/paraspinal muscle mass and ADC measured on MRI.

OBJECTIVE: Evaluate magnetic resonance imaging factors associated with osteoporotic vertebral compression fractures. MATERIALS AND METHODS: We retrospectively reviewed 457 patients' records. Age, sex, and body mass index were recorded. Two blinded readers measured psoas major and paraspinal muscle areas at the L3 vertebral body level on transverse T2-weighted magnetic resonance images and the mean apparent diffusion coefficient values of the non-fractured vertebrae from Th12 to L5. Inter-reader reliability for continuous variables was assessed by intraclass correlation coefficients. RESULTS: We evaluated 210 patients (103 [49.0%] men). The osteoporotic vertebral compression fractures group was older and had lower BMI and smaller psoas major and paraspinal muscle areas than the group without vertebral compression fractures (p < 0.001). The mean apparent diffusion coefficient was weakly correlated with paraspinal muscle area in the osteoporotic vertebral compression fractures group. The intraclass correlation coefficient value was 0.83, and the intraclass correlation coefficients of the psoas major and paraspinal muscles were 0.94 and 0.97, respectively. Multivariate analysis revealed that decreased psoas major and paraspinal muscle areas and increased mean apparent diffusion coefficient values were significantly associated with the presence of osteoporotic vertebral compression fractures (all p < 0.05). Psoas major and paraspinal muscle areas showed relatively high predictive accuracy (57%, 61%). CONCLUSION: Psoas major and paraspinal muscle areas at the L3 level and the mean apparent diffusion coefficient value of non-fractured vertebrae from the Th12 to L5 level were associated with osteoporotic vertebral compression fractures. This may contribute to detecting the potential risk of healthy individuals developing osteoporotic vertebral compression fractures.

Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma.

OBJECTIVE: To evaluate the parameters of support vector machine (SVM) using imaging data generated from the apparent diffusion coefficient (ADC) to differentiate between focal-type autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) when using SVM based on diffusion-weighted imaging. METHODS: The 2D-ADCmean and texture parameters (16 texture features × [non-filter+17 filters]) were retrospectively segmented by 2 readers in 28 patients with f-AIP and 77 patients with pathologically proven PDAC. The diagnostic accuracy of the SVM model was evaluated by receiver operating characteristic curve analysis and calculation of the area under the curve (AUC). Interreader reliability was assessed by intraclass correlation coefficient (ICC). RESULTS: The 2D-ADCmean and 3D-ADCmean were significantly lower in cases of f-AIP (1.10-1.15 × 10-3 mm2/s and 1.21-1.23× 10-3 mm2/s, respectively) vs PDAC (1.29-1.33 × 10-3 mm2/s and 1.41-1.43 × 10-3 mm2/s, respectively), with excellent and good interreader reliability, respectively (ICC = 0.909 and 0.891, respectively). Among the texture parameters, energy with exponential filtering yielded the highest AUC (Reader 1: 74.7%, Reader 2: 81.5%), with fair interreader reliability (ICC = 0.707). The non-linear SVM, a combination of 2D-ADCmean, object volume and exponential-energy showed an AUC value of 96.2% in the testing cohorts. CONCLUSION: Our results suggest that non-linear SVM using a combination of 2D-ADCmean, object volume, and exponential-energy may assist in differentiating f-AIP from PDAC. ADVANCES IN KNOWLEDGE: The radiomics based on an apparent diffusion coefficient value may assist in differentiating f-AIP from PDAC.

Quantitative Histogram Analysis of T2-Weighted and Diffusion-Weighted Magnetic Resonance Images for Prediction of Malignant Thymic Epithelial Tumors.

PURPOSE: To assess the value of histogram analysis for differentiating a high-risk thymic epithelial tumor (TET) from a low-risk TET using T2-weighted images and the apparent diffusion coefficient (ADC). METHODS: Forty-nine patients with histopathologically proven TET after thymectomy were enrolled in this study and retrospectively classified as having low-risk TET (low-risk thymoma) or high-risk TET (high-risk thymoma or thymic carcinoma). Twelve parameters were obtained from the quantitative histogram analysis. The histogram parameters were compared using the Mann-Whitney U test. Diagnostic efficacy was estimated by receiver-operating characteristic curve analysis. RESULTS: Twenty-five patients were classified as having low-risk TET and 24 as having high-risk TET. The mean ADC value showed diagnostic efficacy for differentiating high-risk TET from low-risk TET, with an area under the curve of 0.7, and was better than when using conventional methods alone. CONCLUSION: The ADC-based histogram analysis could help to differentiate between high-risk and low-risk TETs.

3D quantitative analysis of diffusion-weighted imaging for predicting the malignant potential of intraductal papillary mucinous neoplasms of the pancreas.

PURPOSE: To investigate the predictors of intraductal papillary mucinous neoplasms of the pancreas (IPMNs) with high-grade dysplasia, using 2-dimensional (2D) analysis and 3-dimensional (3D) volume-of-interest-based apparent diffusion coefficient (ADC) histogram analysis. MATERIAL AND METHODS: The data of 45 patients with histopathologically confirmed IPMNs with high-grade or low-grade dysplasia were retrospectively assessed. The 2D analysis included lesion-to-spinal cord signal intensity ratio (LSR), minimum ADC value (ADCmin), and mean ADC value (ADCmean). The 3D analysis included the overall mean (ADCoverall mean), mean of the bottom 10th percentile (ADCmean0-10), mean of the bottom 10-25th percentile (ADCmean10-25), mean of the bottom 25-50th percentile (ADCmean25-50), skewness (ADCskewness), kurtosis (ADCkurtosis), and entropy (ADCentropy). Diagnostic performance was compared by analysing the area under the receiver operating characteristic curve (AUC). Inter-rater reliability was assessed by blinded evaluation using the intraclass correlation coefficient. RESULTS: There were 16 and 29 IPMNs with high- and low-grade dysplasia, respectively. The LSR, ADCoverall mean, ADCmean0-10, ADCmean10-25, ADCmean25-50, and ADCentropy showed significant between-group differences (AUC = 72-93%; p < 0.05). Inter-rater reliability assessment showed almost perfect agreement for LSR and substantial agreement for ADCoverall mean and ADCentropy. Multivariate logistic regression showed that ADCoverall mean and ADCentropy were significant independent predictors of malignancy (p < 0.05), with diagnostic accuracies of 80% and 73%, respectively. CONCLUSION: ADCoverall mean and ADCentropy from 3D analysis may assist in predicting IPMNs with high-grade dysplasia.

Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps.

PURPOSE: To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET). MATERIALS AND METHODS: Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs). RESULTS: G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively. CONCLUSION: Texture analysis may aid in differentiating between G1 and G2-3 PNET.

Differentiating Rheumatoid and Psoriatic Arthritis of the Hand: Multimodality Imaging Characteristics.

Accurate diagnosis and therapeutic intervention at an early stage is paramount for the management of rheumatoid arthritis (RA) and psoriatic arthritis (PsA), which are the two major types of inflammatory arthritis that involve the hand joints. As more disease-specific medications are developed, medication selection according to the correct diagnosis becomes more important. A delay in diagnosis and inappropriate medication selection may result in poor functional prognosis. However, clinical differentiation between RA and PsA can be challenging and may become largely dependent on imaging interpretation results. Although there is substantial overlap in the imaging findings of RA and PsA, there are differences in the affected primary target sites, reflected by the various patterns of joint involvement, and different microanatomic localization of abnormalities within a single joint in each disease. Therefore, appropriate use of various imaging modalities and accurate image interpretation add significant value to the diagnosis and treatment process. The synovio-entheseal complex is an important concept for understanding the imaging features of PsA. The authors review the different features of RA and PsA of the hands seen with various imaging modalities, including radiography, US, MRI, and dual-energy CT, with updates on the contemporary role of imaging in diagnosis and treatment. The radiologist should have sufficient knowledge to interpret imaging findings and understand the strengths and weaknesses of each modality to recommend the appropriate imaging method and differentiate both diseases accurately. ©RSNA, 2020.

Breast magnetic resonance imaging for estimation of the tumour extent in patients with pure ductal carcinoma in situ: Comparison between full diagnostic and abbreviated protocols.

PURPOSE: To evaluate the rate of concordance between pathology and preoperative breast MRI performed with an abbreviated protocol (AP) or a full diagnostic protocol (FDP) for estimation of the tumour extent in patients with pure ductal carcinoma in situ (DCIS). METHODS: This retrospective study included 164 patients with pathologically proven DCIS who underwent preoperative breast MRI. Two radiologists independently evaluated the tumour extent on MRI with (FDP) and without the delayed phase (AP) and compared the readings with the pathological tumour extent. The background parenchymal enhancement (BPE) and morphology were also evaluated. Furthermore, the influence of the degree of BPE, presence or absence of B2 and B3 lesions, and pathological DCIS grade on the accuracy of MRI findings was assessed. Concordance between MRI and pathology was evaluated using correlation analysis. RESULTS: Spearman's rank correlation coefficients for the concordance between MRI and pathology were 0.63 (reader 1) and 0.69 (reader 2) with AP and 0.65 and 0.73 (readers 1 and 2, respectively) with FDP. For both readers, the difference in the measured value between FDP and pathology was significantly smaller than that between AP and pathology (p < 0.001). The inter-reader variation in the measured tumour extent was larger with FDP than with AP. The presence of B3 lesions, low-grade DCIS, and moderate/marked BPE lowered the rate of concordance between MRI and pathology. CONCLUSIONS: Our findings suggest that preoperative MRI with FDP is more accurate than that with AP alone for estimation of the tumour extent in patients with pure DCIS.

Differentiation between non-hypervascular pancreatic neuroendocrine tumour and pancreatic ductal adenocarcinoma on dynamic computed tomography and non-enhanced magnetic resonance imaging.

PURPOSE: To determine the differentiating features between non-hypervascular pancreatic neuroendocrine tumour (PNET) and pancreatic ductal adenocarcinoma (PDAC) on dynamic computed tomography (CT) and non-enhanced magnetic resonance imaging (MRI). MATERIAL AND METHODS: We enrolled 102 patients with non-hypervascular PNET (n = 15) or PDAC (n = 87), who had undergone dynamic CT and non-enhanced MRI. One radiologist evaluated all images, and the results were subjected to univariate and multivariate analyses. To investigate reproducibility, a second radiologist re-evaluated features that were significantly different between PNET and PDAC on multivariate analysis. RESULTS: Tumour margin (well-defined or ill-defined) and enhancement ratio of tumour (ERT) showed significant differences in univariate and multivariate analyses. Multivariate analysis revealed a predominance of well-defined tumour margins in non-hypervascular PNET, with an odds ratio of 168.86 (95% confidence interval [CI]: 10.62-2685.29; p < 0.001). Furthermore, ERT was significantly lower in non-hypervascular PNET than in PDAC, with an odds ratio of 85.80 (95% CI: 2.57-2860.95; p = 0.01). Sensitivity, specificity, and accuracy were 86.7%, 96.6%, and 95.1%, respectively, when the tumour margin was used as the criteria. The values for ERT were 66.7%, 98.9%, and 94.1%, respectively. In reproducibility tests, both tumour margin and ERT showed substantial agreement (margin of tumour, κ = 0.6356; ERT, intraclass correlation coefficients (ICC) = 0.6155). CONCLUSIONS: Non-hypervascular PNET showed well-defined margins and lower ERT compared to PDAC, with significant differences. Our results showed that non-hypervascular PNET can be differentiated from PDAC via dynamic CT and non-enhanced MRI.