Prevalence and risk factors of depression among patients with perianal fistulizing Crohn's disease.

BACKGROUND: Psychological distress, especially depression, associated with perianal fistulizing Crohn's disease (PFCD) is widespread and refractory. However, there is a surprising paucity of studies to date that have sought to identify the prevalence and risk factors of depression associated with PFCD. AIM: To estimate the prevalence of depressive symptoms and investigate the depression-related risk factors in patients with PFCD. METHODS: The study was conducted in the form of survey and clinical data collection via questionnaire and specialized medical staff. Depressive symptoms, life quality, and fatigue severity of patients with PFCD were assessed by Patient Health Questionnaire-9, Inflammatory Bowel Disease Patient Quality of Life Questionnaire (IBDQ), and Inflammatory Bowel Disease (IBD) Fatigue Patient Self-assessment Scale. The basic demographic information, overall disease features, perianal clinical information, and laboratory inflammation indicators were also gathered. Multivariate regression analysis was ultimately used to ascertain the risk factors of depression associated with PFCD. RESULTS: A total of 123 patients with PFCD were involved, and 56.91% were suffering from depression. According to multivariate logistic regression analysis, Perianal Disease Activity Index (PDAI) score [odds ratio (OR) = 0.69, 95% confidence interval (CI): 0.50 to 0.95], IBDQ score (OR = 0.93, 95%CI: 0.88 to 0.97), modified Van Assche index (OR = 1.24, 95%CI: 1.01 to 1.53), and IBD Fatigue score (OR = 1.72, 95%CI: 1.23 to 2.42) were independent risk factors of depression-related prevalence among patients with PFCD (P < 0.05). Multiple linear regression analysis revealed that the increasing perianal modified Van Assche index (ß value = 0.166, 95%CI: 0.02 to 0.31) and decreasing IBDQ score (ß value = -0.116, 95%CI: -0.14 to -0.09) were independently associated with the severity of depression (P < 0.05). CONCLUSION: Depressive symptoms in PFCD patients have significantly high prevalence. PDAI score, modified Van Assche index, quality of life, and fatigue severity were the main independent risk factors.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma.

BACKGROUND: One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages. AIM: To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. METHODS: A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model. RESULTS: A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models. CONCLUSION: The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

Diagnostic accuracy of apparent diffusion coefficient to differentiate intrapancreatic accessory spleen from pancreatic neuroendocrine tumors.

BACKGROUND: Intrapancreatic accessory spleen (IPAS) shares similar imaging findings with hypervascular pancreatic neuroendocrine tumors (PNETs), which may lead to unnecessary surgery. AIM: To investigate and compare the diagnostic performance of absolute apparent diffusion coefficient (ADC) and normalized ADC (lesion-to-spleen ADC ratios) in the differential diagnosis of IPAS from PNETs. METHODS: A retrospective study consisting of 29 patients (16 PNET patients vs 13 IPAS patients) who underwent preoperative contrast-enhanced magnetic resonance imaging together with diffusion-weighted imaging/ADC maps between January 2017 and July 2020 was performed. Two independent reviewers measured ADC on all lesions and spleens, and normalized ADC was calculated for further analysis. The receiver operating characteristics analysis was carried out for evaluating the diagnostic performance of both absolute ADC and normalized ADC values in the differential diagnosis between IPAS and PNETs by clarifying sensitivity, specificity, and accuracy. Inter-reader reliability for the two methods was evaluated. RESULTS: IPAS had a significantly lower absolute ADC (0.931 ± 0.773 × 10-3 mm2/s vs 1.254 ± 0.219 × 10-3 mm2/s) and normalized ADC value (1.154 ± 0.167 vs 1.591 ± 0.364) compared to PNET. A cutoff value of 1.046 × 10-3 mm2/s for absolute ADC was associated with 81.25% sensitivity, 100% specificity, and 89.66% accuracy with an area under the curve of 0.94 (95% confidence interval: 0.8536-1.000) for the differential diagnosis of IPAS from PNET. Similarly, a cutoff value of 1.342 for normalized ADC was associated with 81.25% sensitivity, 92.31% specificity, and 86.21% accuracy with an area under the curve of 0.91 (95% confidence interval: 0.8080-1.000) for the differential diagnosis of IPAS from PNET. Both methods showed excellent inter-reader reliability with intraclass correlation coefficients for absolute ADC and ADC ratio being 0.968 and 0.976, respectively. CONCLUSION: Both absolute ADC and normalized ADC values can facilitate the differentiation between IPAS and PNET.

Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma.

Background: Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics. Methods: Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC. Results: A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850). Conclusions: Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS: In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION: The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation.

We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditions. We aim at both speaker separation and dereverberation. Our study first investigates offline utterance-wise speaker separation and then extends to block-online continuous speech separation (CSS). Assuming a fixed array geometry between training and testing, we train deep neural networks (DNN) to predict the real and imaginary (RI) components of target speech at a reference microphone from the RI components of multiple microphones. We then integrate multi-microphone complex spectral mapping with minimum variance distortionless response (MVDR) beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online CSS. Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry. State-of-the-art separation performance is obtained on the simulated two-talker SMS-WSJ corpus and the real-recorded LibriCSS dataset.

Evidence of motor injury due to damaged corticospinal tract following acute hemorrhage in the basal ganglia region.

The integrity of the corticospinal tract (CST) is significantly affected following basal ganglia haemorrhage. We aimed to assess the local features of CST and to effectively predict motor function by diffusion characteristics of CST in patients with motor injury following acute haemorrhage in the acute basal ganglia region. We recruited 37 patients with paresis of the lateral limbs caused by acute basal ganglia haemorrhage. Based on the automated fiber quantification method to track CST, assessed the character of each CST segment between the affected and contralateral sides, and correlated these with the Fugl-Meyer (FM) and Barthel Index (BI) scores at 6 months after onset. The fractional anisotropy (FA) values of the injured side of CST showed a significantly lower FA than the contralateral side along the tract profiles (p < 0.05, corrections for multiple comparisons). The FA values of each site at the internal capsule, closed corona radiata were positively correlated with the FM and BI score at 6 months after onset (p < 0.001, respectively). Our findings assessed the character of CST vividly in detail and dementated the primary sites of CST can predict the long-term outcome of motor function. This study may facilitate future clinical and cognitive studies of acute haemorrhage.

Hysterosalpingo-contrast-sonography vs. magnetic resonance-hysterosalpingography for diagnosing fallopian tubal patency: A systematic review and meta-analysis.

PURPOSE: To compare hysterosalpingo-contrast-sonography (HyCoSy) and magnetic resonance-hysterosalpingography (MR-HSG) in the diagnosis of fallopian tubal patency. MATERIALS AND METHODS: The databases of PubMed, Embase, and the Cochrane Library were searched for records up to November 30, 2019. Studies involved in the diagnostic detection of HyCoSy or MR-HSG for fallopian tubal patency using conventional HSG or laparoscopy as the reference test were included. Data was analyzed by meta-analysis. We compared sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (sROC) plots of both HyCoSy and MR-HSG. Quality was assessed using the QUADAS-2 tool. RESULTS: The analysis included 24 articles involving 1340 patients. HyCoSy was studied in 17 studies, and MR-HSG was studied in seven studies. For HyCoSy in diagnosis of fallopian tubal patency, pooled sensitivity was 89 % (95 % confidence interval [CI], 87 %-91 %), and specificity was 93 % (95 % CI, 91 %-94 %). For MR-HSG in diagnosis of fallopian tubal patency, pooled sensitivity was 100 % (95 % CI, 98 %-100 %), and specificity was 82 % (95 % CI, 74 %-89 %). The sROC showed similar diagnostic accuracy for MR-HSG and HyCoSy. 3D/4D HyCoSy with ultrasound microbubbles had equal sensitivity (95 % vs. 100 %, P = 0.186) and significantly higher specificity (94 % vs. 82 %, P = 0.005) compared with MR-HSG. CONCLUSIONS: HyCoSy and MR-HSG showed similar overall diagnostic performance for diagnosing fallopian tubal patency. 3D/4D HyCoSy with ultrasound microbubbles could significantly improve the diagnostic specificity of HyCoSy.

Deep Learning Based Target Cancellation for Speech Dereverberation.

This study investigates deep learning based single- and multi-channel speech dereverberation. For single-channel processing, we extend magnitude-domain masking and mapping based dereverberation to complex-domain mapping, where deep neural networks (DNNs) are trained to predict the real and imaginary (RI) components of the direct-path signal from reverberant (and noisy) ones. For multi-channel processing, we first compute a minimum variance distortionless response (MVDR) beamformer to cancel the direct-path signal, and then feed the RI components of the cancelled signal, which is expected to be a filtered version of non-target signals, as additional features to perform dereverberation. Trained on a large dataset of simulated room impulse responses, our models show excellent speech dereverberation and recognition performance on the test set of the REVERB challenge, consistently better than single- and multi-channel weighted prediction error (WPE) algorithms.

Complex Spectral Mapping for Single- and Multi-Channel Speech Enhancement and Robust ASR.

This study proposes a complex spectral mapping approach for single- and multi-channel speech enhancement, where deep neural networks (DNNs) are used to predict the real and imaginary (RI) components of the direct-path signal from noisy and reverberant ones. The proposed system contains two DNNs. The first one performs single-channel complex spectral mapping. The estimated complex spectra are used to compute a minimum variance distortion-less response (MVDR) beamformer. The RI components of beamforming results, which encode spatial information, are then combined with the RI components of the mixture to train the second DNN for multi-channel complex spectral mapping. With estimated complex spectra, we also propose a novel method of time-varying beamforming. State-of-the-art performance is obtained on the speech enhancement and recognition tasks of the CHiME-4 corpus. More specifically, our system obtains 6.82%, 3.19% and 2.00% word error rates (WER) respectively on the single-, two-, and six-microphone tasks of CHiME-4, significantly surpassing the current best results of 9.15%, 3.91% and 2.24% WER.

Differentiation Between G1 and G2/G3 Phyllodes Tumors of Breast Using Mammography and Mammographic Texture Analysis.

Purpose: To determine the potential of mammography (MG) and mammographic texture analysis in differentiation between Grade 1 (G1) and Grade 2/ Grade 3 (G2/G3) phyllodes tumors (PTs) of breast. Materials and methods: A total of 80 female patients with histologically proven PTs were included in this study. 45 subjects who underwent pretreatment MG from 2010 to 2017 were retrospectively analyzed, including 14 PTs G1 and 31 PTs G2/G3. Tumor size, shape, margin, density, homogeneity, presence of fat, or calcifications, a halo-sign as well as some indirect manifestations were evaluated. Texture analysis features were performed using commercial software. Receiver operating characteristic curve (ROC) was used to determine the sensitivity and specificity of prediction. Results: G2/G3 PTs showed a larger size (>4.0 cm) compared to PTs G1 (64.52 vs. 28.57%, p = 0.025). A strong lobulation or multinodular confluent was more common in G2/G3 PTs compared to PTs G1 (64.52 vs. 14.29%, p = 0.004). Significant differences were also observed in tumors' growth speed and clinical manifestations (p = 0.007, 0.022, respectively). Ten texture features showed significant differences between the two groups (p < 0.05), Correlation_AllDirection_offset7_SD and ClusterProminence_AllDirection_offset7_SD were independent risk factors. The area under the curve (AUC) of imaging-based diagnosis, texture analysis-based diagnosis and the combination of the two approaches were 0.805, 0.730, and 0.843 (90.3% sensitivity and 85.7% specificity). Conclusions: Texture analysis has great potential to improve the diagnostic efficacy of MG in differentiating PTs G1 from PTs G2/G3.

Two-stage Deep Learning for Noisy-reverberant Speech Enhancement.

In real-world situations, speech reaching our ears is commonly corrupted by both room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also pose a serious problem to many speech-related applications, including automatic speech and speaker recognition. In order to deal with the combined effects of noise and reverberation, we propose a two-stage strategy to enhance corrupted speech, where denoising and dereverberation are conducted sequentially using deep neural networks. In addition, we design a new objective function that incorporates clean phase during model training to better estimate spectral magnitudes, which would in turn yield better phase estimates when combined with iterative phase reconstruction. The two-stage model is then jointly trained to optimize the proposed objective function. Systematic evaluations and comparisons show that the proposed algorithm improves objective metrics of speech intelligibility and quality substantially, and significantly outperforms previous one-stage enhancement systems.

Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance.

PURPOSE: The purpose of this study was to evaluate the performance of magnetic resonance imaging (MRI) findings and texture parameters for prediction of the histopathologic grade of pancreatic neuroendocrine tumors (PNETs) with 3-T magnetic resonance. PATIENTS AND METHODS: PNETs are classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity. A total of 77 patients with pathologically confirmed PNETs met the inclusion criteria. Texture analysis (TA) was applied to T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) maps. Patient demographics, MRI findings, and texture parameters were compared among three different histopathologic subtypes by using Fisher's exact tests or Kruskal-Wallis test. Then, logistic regression analysis was adopted to predict tumor grades. ROC curves and AUCs were calculated to assess the diagnostic performance of MRI findings and texture parameters in prediction of tumor grades. RESULTS: There were 31 G1, 29 G2, and 17 G3 patients. Compared with G1, G2/G3 tumors showed higher frequencies of an ill-defined margin, a predominantly solid tumor type, local invasion or metastases, hypo-enhancement at the arterial phase, and restriction diffusion. Four T2-based (inverse difference moment, energy, correlation, and differenceEntropy) and five DWI-based (correlation, contrast, inverse difference moment, maxintensity, and entropy) TA parameters exhibited statistical significance among PNETs (P<0.001). The AUCs of six predicting models on T2WI and DWI ranged from 0.703-0.989. CONCLUSION: Our data indicate that MRI findings, including tumor margin, texture, local invasion or metastases, tumor enhancement, and diffusion restriction, as well as texture parameters can aid the prediction of PNETs grading.

Chemical shift magnetic resonance imaging for distinguishing minimal-fat renal angiomyolipoma from renal cell carcinoma: a meta-analysis.

OBJECTIVES: To determine the performance of chemical shift signal intensity index (CS-SII) values for distinguishing minimal-fat renal angiomyolipoma (mfAML) from renal cell carcinoma (RCC) and to assess RCC subtype characterisation. METHODS: We identified eligible studies on CS magnetic resonance imaging (CS-MRI) of focal renal lesions via PubMed, Embase, and the Cochrane Library. CS-SII values were extracted by lesion type and evaluated using linear mixed model-based meta-regression. RCC subtypes were analysed. Two-sided p value <0.05 indicated statistical significance. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. RESULTS: Eleven articles involving 850 patients were included. Minimal-fat AML had significantly higher CS-SII value than RCC (p < 0.05); there were no significant differences between mfAML and clear cell RCC (cc-RCC) (p = 0.112). Clear cell RCC had a significantly higher CS-SII value than papillary RCC (p-RCC) (p < 0.001) and chromophobe RCC (ch-RCC) (p = 0.045). The methodological quality was relatively high, and Begg's test data points indicated no obvious publication bias. CONCLUSIONS: The CS-SII value for differentiating mfAML from cc-RCC remains unproven, but is a promising method for differentiating cc-RCC from p-RCC and ch-RCC. KEY POINTS: • RCC CS-SII values are significantly lower than those of mfAML overall. • CS-SII values cannot aid differentiation between mfAML and cc-RCC. • CS-SII values might help characterise RCC subtypes.

Atypical choroid plexus papilloma: clinicopathological and neuroradiological features.

Background Atypical choroid plexus papilloma (APP) is a rare, newly introduced entity with intermediate characteristics. To date, few reports have revealed the magnetic resonance (MR) findings. Purpose To analyze the clinicopathological and MR features of APP. Material and Methods The clinicopathological data and preoperative MR images of six patients with pathologically proven APP were retrospectively reviewed. The MR features including tumor location, contour, signal intensity, degree of enhancement, intratumoral cysts, and necrosis; and flow voids, borders, peritumoral edema, and associated hydrocephalus were analyzed. Results The APP were located in the ventricle (n = 4) and cerebellopontine angle (CPA, n = 2). Tumor dissemination along the spinal subarachnoid space was found in one patient. The tumors appeared as milt-lobulated (n = 5) or round mass (n = 1), with slightly heterogeneous signals (n = 5) or mixed signals (n = 1) on T1-weighted and T2-weighted images. Heterogeneous and strong enhancement were found in five cases on contrast-enhanced images. Three of four intraventricular tumors had a partly blurred border with ventricle wall. Four tumors had mild to moderate extent of surrounding edema signals. A slight hydrocephalus was seen in four patients. Incomplete capsule was seen in four tumors at surgery. Histopathologically, mild nuclear atypia was seen in all tumors with a mitotic rate of 2-5 per 10 high-power fields. Conclusion APP should be included in the differential diagnosis when an intraventricular or CPA tumor appearing as a multi-lobulated solid mass with slight heterogeneity, heterogeneous strong enhancement, partly blurred borders, mild to moderate peritumoral edema, or slight hydrocephalus are present.

MR lymphangiography at 3.0 Tesla to assess the function of inguinal lymph node in low extremity lymphedema.

PURPOSE: To assess the function of inguinal lymph node (ILN) in low extremity lymphedema (LEL) with dynamic enhanced MR lymphangiography (MRL). MATERIALS AND METHODS: Sixty-four patients with clinically diagnosed LEL underwent MRL examinations. The lymph drainage patterns were separated into four subtypes. The peak time to enhancement and the contrast ratio of the ILN were calculated 35 min following contrast agent administration. RESULTS: Dilated lymphatic vessels could be observed in all subjects. Type I-IV drainage patterns were observed 25% (16/64), 45.31% (29/64), 17.19% (11/64), and 12.5% (8/64), respectively. The ILN in the edematous limbs could be observed in 93.75% (60/64) of subjects. The peak time to enhancement was correlated with clinical stages (P < 0.001) of the disease, lymph drainage patterns (P < 0.001), and the duration of lymphedema (P < 0.001). The contrast ratios were significantly different at each time point (P < 0.001) and significantly correlated with the lymphatic drainage patterns (P < 0.001). CONCLUSION: MRL could provide useful information for evaluating the functional status of the ILN. Data presented here demonstrate that the functionality of the ILN is related to the clinical stage of the disease, lymphatic drainage patterns, and the duration of lymphedema.

[Assessment of the diagnostic value of CT and X-ray enterography for small intestinal Crohn disease].

OBJECTIVE: To investigate the value of CT and X-ray enterography in the diagnosis of small intestinal Crohn disease(CD). METHODS: Data of 39 CD cases confirmed by surgery and pathology who underwent CT and X-ray enterography were analyzed retrospectively. All the patients had complete CT data, 28 cases had X-ray intestinal barium meal data, and 18 had sinus tract enterography. RESULTS: CT enterography showed mural thickening(>4 mm) in 34(87.2%) patients, mural gas in 7(17.9%), mural edema in 7(17.9%), mural fat in 4(10.3%), increased enhancement of bowel wall(>10 HU) in 37(94.9%), multiple segmental lesions in 33(84.6%), single segmental lesions in 6(15.4%), mesenteric lymphadenopathy(>5 mm) in 13(33.3%), vascular bundle thickening in 9(23.1%), cellulitis in 12(30.8%), peritoneal abscess in 10(25.6%), phlegmon in 8(20.5%), incomplete intestinal obstruction in 14(35.9%), seroperitoneum in 22(56.4%), and fistulization in 4(10.3%). CT enterography did not demonstrate the change of mucosa such as strip ulcer or cobblestone. Among the 28 cases of small bowel X-ray enterography, 23 cases(82.1%) presented with multiple segmental lesions, 5(17.9%) with single segmental lesions, 18(64.3%) with strip ulcer, 16(57.1%) with cobblestones, 4(14.3%) with intestinal fistula, while no bowel wall and extraintestinal complication of CD disease was observed. Among the 18 cases of sinus tract enterography, 13 cases (72.2%) presented with intestinal fistula, 12(66.7%) with peritoneal abscess, 8(44.4%) with sinus tract. CONCLUSIONS: CT enterography can demonstrate exactly the diseased bowel wall and extraintestinal complication of CD disease, which is important to evaluate the extent of CD and guide the treatment, however strip ulcer and cobblestone sign cannot be demonstrated. The X-ray enterography is available to demonstrate the characteristic changes of CD such as trip ulcers and cobblestones, but is difficult to show the bowel wall and extraintestinal inflammatory mass and abscesses. The sinus tract enterography is easy to demonstrate the intestinal fistula and intra-abdominal abscess. Combination of these methods is more beneficial to guild the diagnosis and treatment.

The multislice CT findings of renal carcinoma associated with XP11.2 translocation/TFE gene fusion and collecting duct carcinoma.

BACKGROUND: Renal cell carcinoma associated with Xp11.2 translocation and TFE gene fusion (Xp11.2/TFE RCC), and collecting duct carcinoma (CDC) are uncommon subtypes of renal cell carcinomas. PURPOSE: To investigate the multislice CT (MSCT) characteristics of these two tumor types. MATERIAL AND METHODS: Nine patients with Xp11.2/TFE RCC and 10 patients with CDC were studied retrospectively. MSCT was undertaken to investigate differences in tumor characteristics and enhancement patterns. RESULTS: All patients had single tumors centered in the renal medulla. Two patients with each tumor type had lymph node involvement and there was a single case of hepatic metastasis (Xp11.2/TFE RCC). The mean tumor diameter of Xp11.2/TFE RCC tumors was significantly larger than for CDC tumors. Two patients with Xp11.2/TFE RCC had cystic components as did eight patients with CDC (P < 0.05). Calcifications were present in six patients, each with CDC. Clear tumor boundaries were visible in two patients with CDC and in nine with Xp11.2/TFE RCC (P < 0.05). The density of Xp11.2/TFE RCC tumors was greater than that of CDC tumors, normal renal cortex, or medulla on unenhanced CT. Enhancement was higher with Xp11.2/TFE RCC than with CDC tumors during all phases. Xp11.2/TFE RCC enhancement was higher than in the renal medulla during cortical and medullary phase but lower than in normal renal medulla during the delayed phase. CDC tumor enhancement was lower than that for normal renal medulla during all enhanced phases. CONCLUSION: Both tumor types originated from the renal medulla. Distinguishing features included density on unenhanced CT, enhancement patterns, and capsule signs. Identifying these differences may aid diagnosis.

Dual-energy computed tomography angiography for evaluating the renal vascular variants.

BACKGROUND: Recognizing renal vascular variants preoperatively is important in order to avoid vascular complications during surgery. This study aimed to investigate the renal vascular variants with dual-energy computed tomography (DECT) angiography to provide valuable information for surgery. METHODS: A total of 378 patients underwent DECT. The number, size, course and relationships of the renal vessels were retrospectively observed from the scans. Anomalies of renal arteries and veins were recorded and classified. Multiplanar reformations (MPR), maximum intensity projections (MIP), and volume renderings (VR) were used for analysis. RESULTS: In 378 patients (756 kidneys), renal artery variations were discovered and recorded in 123 kidneys (16.3%, 123/756) of 106 patients (28.0%, 106/378). Type IB (early branches of the only one main renal artery) and IC (accessory renal artery with only one main renal artery) were found most frequently with an incidence of 11.4% (43/378) and 14.5% (55/378). The incidence of renal artery variations in the left kidney was not statistically different than in the right kidney (12.4% vs. 11.1%). The incidence of renal vein variations was detected in 104 patients (27.5%, 104/378). The incidence of venous variants in the right kidney was higher than in the left kidney (20.1% vs. 7.4%), but left renal vein variations were more complex. Variants of the left renal vein were detected in 28 patients including type 1 (circumaortic left renal vein) in eight cases, type 2 (retroaortic left renal vein) in seven cases, type 3 (abnormal reflux) in six cases, type 4 (late venous confluence of left renal vein) in five cases, and type 5 (rare type) in two cases. The frequency of left renal vein variation associated with the left renal accessory artery was significantly higher than with early branches of the left renal artery (P = 0.037). CONCLUSIONS: The renal vascular variants are rather common and complex. DECT angiography can demonstrate the precise anatomy of the renal vessels, which is a benefit for renal transplantation or other renal operations.

[Application of apparent diffusion coefficient ratio in the diagnosis of bladder cancer grading pre-operation].

OBJECTIVE: To explore the value of apparent diffusion coefficient (ADC) ratio in the diagnosis of bladder cancer pre-operation by analyzing its differences among different grades of bladder cancer. METHODS: A total of 52 cases of bladder cancer were all definitely diagnosed with histological results.Routine examinations of magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) were performed preoperatively on each patient. ADC map was constructed in work station and ADC values of tumor and internal obturator muscle were measured (b = 800 s/mm(2)).Ratio of ADC was calculated with internal obturator muscle as reference site. Then the relationship between ADC ratio and bladder cancer grade was analyzed. RESULTS: Mean ratio of ADC of all tumors was 0.98±0.35, G1 (1.12±0.21) and G2 (0.67±0.29), the sensitivity and specificity of ADC ratio was 90.2% and 85.3% respectively with an optimal threshold of 0.96. The ratios of ADC of low-grade group were significantly higher than those of high-grade group while the values of non-muscle-invasive group were significantly higher than those of muscle-invasive group. The ratios of ADC of tumor were inversely associated with the malignancy degree of bladder cancer (r = -0.845, P < 0.05). CONCLUSION: The ratio of ADC of bladder cancer reflects the lesion tissue properties. And its measurement plays an important role in the diagnosis of bladder cancer grading pre-operation.