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PURPOSE We aimed to characterize the clinical and multiphase computed tomography (CT) features of the distinguishing endophytic clear cell renal cell carcinoma (ECCRCC) from endophytic renal urothelial carcinoma (ERUC). METHODS Data from 44 patients (35 men and 9 women) with ECCRCC and 21 patients (17 men and 4 women) with ERUC were retrospectively assessed. The mean patient age was 55 years (48.25- 59.50 years) and 68 years (63.00-73.00 years), respectively. Univariate and multivariate logistic regression analyses were performed to determine independent predictors for ECCRCC and to construct a predictive model that comprised clinical and CT characteristics for the differential diagnosis of ECCRCC and ERUC. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The independent predictors of ECCRCC were heterogeneous enhancement (odds ratio [OR]=0.027, P=.005), hematuria (OR for gross hematuria=53.995, P=.003; OR for microscopic hematuria=31.126, P = .027), and an infiltrative growth pattern (OR=24.301, P = .022). The AUC of the predictive model was 0.938 (P < .001, sensitivity=84.10%, specificity=95.20%), which had a better diagnostic performance than heterogeneous enhancement (AUC=0.766, P=.001, sensitivity=81.82%, specificity=71.43%), hematuria (AUC=0.786, P < .001, sensitivity=81.82%, specificity=66.67%), and infiltrative growth pattern (AUC=0.748, P=.001, sensitivity=90.48%, specificity=59.09%). CONCLUSION The independent predictors, as well as the predictive model of CT and clinical characteristics, may assist in the differential diagnosis of ECCRCC and ERUC and provide useful information for clinical decision-making. [ABSTRACT FROM AUTHOR]

Objective: To determine whether high-speed T2-corrected multiecho (HISTO) sequences can quantify bone marrow edema (BME) in the capitate bone in rheumatoid arthritis (RA), and whether the HISTO fat fraction (FF) reflects therapeutic effectiveness. Methods: In this prospective study, 25 RA patients (19 women; average age, 45.08 ± 13.48 years) underwent 3.0-T MRI with HISTO at the baseline and after 4, 8, and 12 weeks of treatment. Rheumatoid factor (RF), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), platelet (PLT) count, and 28-joint Disease Activity Score using ESR (DAS28-ESR) were recorded on the day of each MRI examination by a rheumatologist blinded to the MRI findings. In addition, 21 healthy subjects (15 women; age, 49.17 ± 6.56 years) underwent only the HISTO sequence at a single time point. Results: HISTO FF values were significantly higher in the control group (74.5% ± 3.1%; range, 68.6–79.3%) than in the patient group (55.8% ± 17.7%; range, 15.6–79.0%) at the baseline (independent-samples t-test: t = 5.257, P = 0.000). The changes in HISTO FF and DAS28-ESR showed moderate negative correlations with each other at 4, 8, and 12 weeks, and all of them were statistically significant (P < 0.05). As the HISTO FF increased, the DAS28-ESR decreased. Conclusion: The HISTO sequence can measure the bone marrow FF of the wrist joint bones in RA patients. The HISTO FF value increased as the DAS28-ESR decreased. The HISTO sequence may help quantify BME in RA and help monitor the effectiveness of RA treatment. Key Points •The HISTO sequence could measure the bone marrow FF of the wrist joint bones. •FF value increased as the DAS28-ESR decreased in RA patients. •The HISTO sequence can monitor the therapeutic effect of RA. [ABSTRACT FROM AUTHOR]

Presents a theoretical analysis of the ferroelectric layer interface wave in a multiple domain layered structure. Plate modes in piezoelectric multilayered structures; Cases of the interaction of multiple domain layers; Orientation of domain and the coordination system.

Background: Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) is usually used for the semi-quantitative evaluation of joint changes in Rheumatoid Arthritis (RA). However, this method cannot evaluate early changes in bone marrow edema (BME)., Objective: To determine whether T1 mapping of wrist BME predicts early treatment response in RA., Methods: This study prospectively enrolled 48 RA patients administered oral anti-rheumatic drugs. MRI of the most severely affected wrist was performed before and after 4 (48 patients) and 8 weeks of treatment (38 patients). Mean T1 values of BME in the lunate, triangular, and capitate bones; RAMRIS for each wrist; Erythrocyte-Sedimentation Rate (ESR); and 28-joint Disease Activity Score (DAS28)-ESR score were analyzed. Patients were divided into responders (4 weeks, 30 patients; 8 weeks, 32 patients) and non-responders (4 weeks, 18 patients; 8 weeks, 6 patients), according to EULAR response criteria. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of T1 values., Results: ESR and DAS28-ESR were not correlated with T1 value and RAMRIS at each examination (P > 0.05). Changes in T1 value and DAS28-ESR relative to the baseline were moderately positively correlated with each other at 4 and 8 weeks (r = 0.555 and 0.527, respectively; P < 0.05). At 4 weeks, the change and rate of change in T1 value significantly differed between responders and non-responders (-85.63 vs. -19.92 ms; -12.89% vs. -2.81%; P < 0.05). The optimal threshold of the rate of change in T1 value at 4 weeks for predicting treatment response was -5.32% (area under the ROC curve, 0.833; sensitivity, 0.900; specificity, 0.667)., Conclusion: T1 mapping provides a new imaging method for monitoring RA lesions; changes in wrist BME T1 values reflect early treatment response., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

PURPOSE We aimed to characterize the clinical and multiphase computed tomography (CT) features of the distinguishing endophytic clear cell renal cell carcinoma (ECCRCC) from endophytic renal urothelial carcinoma (ERUC). METHODS Data from 44 patients (35 men and 9 women) with ECCRCC and 21 patients (17 men and 4 women) with ERUC were retrospectively assessed. The mean patient age was 55 years (48.25- 59.50 years) and 68 years (63.00-73.00 years), respectively. Univariate and multivariate logistic regression analyses were performed to determine independent predictors for ECCRCC and to construct a predictive model that comprised clinical and CT characteristics for the differential diagnosis of ECCRCC and ERUC. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The independent predictors of ECCRCC were heterogeneous enhancement (odds ratio [OR]=0.027, P=.005), hematuria (OR for gross hematuria=53.995, P=.003; OR for microscopic hematuria=31.126, P = .027), and an infiltrative growth pattern (OR=24.301, P = .022). The AUC of the predictive model was 0.938 (P < .001, sensitivity=84.10%, specificity=95.20%), which had a better diagnostic performance than heterogeneous enhancement (AUC=0.766, P=.001, sensitivity=81.82%, specificity=71.43%), hematuria (AUC=0.786, P < .001, sensitivity=81.82%, specificity=66.67%), and infiltrative growth pattern (AUC=0.748, P=.001, sensitivity=90.48%, specificity=59.09%). CONCLUSION The independent predictors, as well as the predictive model of CT and clinical characteristics, may assist in the differential diagnosis of ECCRCC and ERUC and provide useful information for clinical decision-making.

Background: Distinguishing exophytic renal urothelial carcinoma (ERUC) from exophytic renal clear-cell carcinoma (ERCCC) with collecting system invasion may be difficult as they involve similar locations and collecting system invasion., Objective: The study aimed to characterize the clinical data and computed tomography (CT) features that can aid in differentiating ERUC from ERCCC., Methods: Data from 17 patients with ERUC and 222 patients with ERCCC were retrospectively assessed. CT and clinical features exhibiting significant differences in t-tests/Mann-Whitney U-test and chi-square tests/Fisher's exact tests were analyzed using receiver operating characteristic (ROC) curves. Variables with an area under the curve (AUC) <0.7 were excluded. Univariate logistic regression analysis was used to analyze the associations of CT and clinical features with ERUC or ERCCC. Variables with odds ratio (OR) values being close to 1 in univariate logistic regression were excluded from multivariate logistic regression. A predictive model was then constructed based on the predictors (p<0 in multivariate logistic regression). Differential diagnostic performance was assessed with AUC values., Results: Multivariate logistic regression analysis identified preserving reniform contour (OR: 45.27, 95% confidence interval [CI]: 4.982-411.39) and infiltrative growth pattern (OR: 21.741, 95% CI: 1.898-249.049) as independent predictors that can be used to distinguish ERUC from ERCCC. AUC values for preserving reniform contour, infiltrative growth pattern, and Model-1 were 0.907 (95% CI: 0.817-0.998), 0.837 (95% CI: 0.729-0.946), and 0.947 (95% CI: 0.874-1), respectively., Conclusion: The independent predictors and predictive model may play an important role in preoperative differentiation between ERUC and ERCCC., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)