Dual-energy computed tomography in supporting prostatic artery embolization for benign prostatic hyperplasia.

Objective: Our study aims to evaluate the value of 256-slice dual-energy computed tomography (DECT) in supporting prostatic artery embolization (PAE) under digital subtraction angiography (DSA) for benign prostatic hyperplasia (BPH). Methods: The study was conducted on 88 patients who underwent PAE to treat BPH from January 2022 to November 2023. Of these, 38 patients who had PAE without DECT were placed in group 1, while the other 50 patients with pre-interventional DECT were assigned to group 2. The results of DECT imaging of the prostate artery (PA) were compared with the results of DSA imaging. Test for statistically significant differences between the variables of the two research groups using the T - student test and Mann-Whitney test algorithms with p < 0.05 corresponding to a 95% confidence interval. The data were analyzed according to medical statistical methods using SPSS 20.0 software. Results: DECT can detect the PA origin in 96.1% of cases, identify atherosclerosis at the root of the artery with a sensitivity of 66.7% and a specificity of 89.5%, and present anastomosis with a sensitivity of 72.7% and a specificity of 72.2%. There is no statistically significant difference in PA diameter on DECT compared to DSA with 95% confidence. Group 2 used DECT for 3D rendering of the PA before PAE had procedure time reduced by 25.8%, fluoroscopy time reduced by 23.2%, dose-area product (DAP) reduced by 25.6%, contrast medium volume reduced by 33.1% compared to group 1 not using DECT, statistically significant with 95% confidence. Conclusion: DECT is a valuable method for planning before PAE to treat BPH. 3D rendering DECT of PA provides anatomical information that minimizes procedure time, fluoroscopy time, dose-area product, and contrast medium volume.

Differentiation of glioblastoma and primary central nervous system lymphomas using multiparametric diffusion and perfusion magnetic resonance imaging.

The present study aimed to determine whether combining diffusion-weighted (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted (DSC-PWI) magnetic resonance imaging (MRI) could differentiate between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). The present retrospective study evaluated 45 patients with histologically confirmed brain tumors, of which 18 had PCNSLs and 27 had GBMs. All patients underwent conventional, DWI, and DSC-PWI MRIs before the surgical removal of the lesion or stereotactic biopsy. The solid tumor component, peritumoral edema, and abnormal white matter were measured in three regions of interest to evaluate relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and DWI. In conventional MRI, there were significant differences in tumor numbers, tumor enhancement type, tumor necrosis, hemorrhage and open-ring sign between GBM and PCNSL. Solid tumor ADC and rCBV values (ADCt and rCBVt, respectively) and their ratios with abnormal white matter amounts were significantly higher in GBM cases than in PCNSL cases (P<0.05). The rCBV value for peritumoral edema (rCBVe) and its ratio with abnormal white matter amount (rCBVe/n) were significantly higher in GBM cases than in PCNSL cases (P<0.05). However, ADC values did not differ significantly for peritumoral edema. DWI values did not differ significantly. Combining rCBVt and rCBVe/n provided a perfect area under the receiver operating characteristic curve of 1.00, with 100% sensitivity and 100% specificity for distinguishing GBM from PCNSL. In the results of the present study, the major criterion in the decision-making process distinguishing PCNSL from GBM was the combined rCBVt and rCBVe/n parameter. A minor criterion was the ADCt value of the lesion.

The value of quantitative magnetic resonance imaging signal intensity in distinguishing between spinal meningiomas and schwannomas.

Background: Prior studies have suggested a number of the subjective visual characteristics that help distinguish between spinal meningiomas and schwannomas on magnetic resonance imaging and computed tomography; however, objective quantification of the signal intensity can be useful information. This study assessed whether quantitative magnetic resonance imaging (MRI) signal intensity (SI) measurements could distinguish intradural-extramedullary schwannomas from meningiomas. Methods: From July 2019 to September 2021, 54 patients with intradural-extramedullary tumors (37 meningiomas and 17 schwannomas) underwent surgery, and tumors were verified pathologically. Defined regions of interest were used to quantify SI values on T1- (T1W) and T2-weighted images (T2W). Receiver operating characteristic curve analysis was used to obtain cutoff values and calculate the area under the curve (AUC), sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV). Results: Both Maximum (T2max) and mean (T2mean) T2W SI values demonstrated outstanding (AUC: 0.91) abilities to differentiate meningiomas from schwannomas with Se, Sp, PPV, and NPV values of 94.6%, 70.6%, 87.5%, and 85.7%, respectively, for T2max and 81.1%, 88.2%, 93.8%, and 68.2% for T2mean. The maximum SI value on contrast-enhanced T1W (T1CEmax) and the T2W tumor: fat SI ratio (rTF) demonstrated acceptable abilities (AUC: 0.73 and 0.79, respectively) to differentiate meningiomas from schwannomas with Se, Sp, PPV, and NPV values of 94.6%, 70.6%, 87.5%, and 85.7%, respectively, for T1CEmax and 81.1%, 88.2%, 93.8%, and 68.2% for rTF. Conclusions: Quantitative SI values (T2max, T2mean, T2min, T1CEmax, rTF) can be used to differentiate intradural-extramedullary schwannomas from meningiomas.