A Practical Index to Distinguish Backwash Ileitis From Crohn's Terminal Ileitis in MR Enterography.

BACKGROUND: Differentiating ulcerative colitis-associated "backwash" ileitis (BWI) from Crohn's terminal ileitis (CTI) is a diagnostic challenge and highly affects patient's management. This study aimed to investigate magnetic resonance enterography (MRE) features including ileocecal valve patency index (ICPI) in patients with BWI and CTI and distinguish these entities based on MRE findings. METHODS: After obtaining institutional review board approval, we reviewed 1654 MREs; 60 patients with pathologically proven BWI (n = 30) and CTI (n = 30) were enrolled. Two radiologists who were blinded to the clinical diagnosis analyzed MREs. We evaluated bowel wall thickness and enhancement pattern, ileocecal valve (ICV) diameter, and lip thickness. Ileocecal valve patency index-T and ICPI-C were calculated to normalize the ICV diameter with respect to terminal ileum (TI) and cecum, respectively. An additional group of non-BWI-UC patients (n = 30) was also included to validate indices. RESULTS: Circumferential mural thickening (90% vs 1%, P < .001) and inner-wall enhancement (P < .001) of TI were more frequent in BWI patients than CTI. Serosal irregularity (53% vs 13%, P = .002), higher mural thickness (5mm vs 3mm, P < .001), and asymmetric hyperenhancement (P < .001) of TI were more prevalent in CTI than BWI. Ileocecal valve patency and lip atrophy were significantly higher in BWI than CTI and non-BWI-UC groups (both P < .001). Ileocecal valve patency indices-C and ICPI-T indices were able to accurately distinguish BWI from CTI (area under the ROC curve [AUC], 0.864 and 0.847 for ICPI-T and ICPI-C, respectively) and non-BWI-UC (AUC, 0.777 and 0.791 for ICPI-T and ICPI-C, respectively). Ileocecal valve patency indices-T  ≥31.5% were 100% specific to distinguish BWI from CTI, but sensitivity was 63%. CONCLUSIONS: Magnetic resonance enterography features of ICV and TI can accurately differentiate BWI from CTI. Two practical indices introduced in this study showed high specificity to distinguish BWI from CTI.

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

Mesenteric lymph nodes in MR enterography: are they reliable followers of bowel in active Crohn's disease?

OBJECTIVES: To demonstrate magnetic resonance enterography (MRE) features of mesenteric lymph nodes (LN) in patients with Crohn's disease (CD) and investigate whether they follow enhancement or apparent diffusion coefficient (ADC) parameters of bowel. METHODS: This study was approved by the institutional review board. A total of 788 MREs from patients with CD were retrospectively reviewed. Eighty-eight patients, aged 16-66 years, including 59 active cases, were enrolled based on inclusion criteria. In each MRE, two segments (normal and abnormal) and two LNs (regional and non-regional) were independently suggested, consensually chosen, and analyzed by two radiologists. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated to assess signal intensities (SI) at 30, 60 and 180 s after contrast administration, as well as slope of enhancement (SOE). Enhancement parameters and ADC values were compared. RESULTS: Regional LNs showed significantly higher SI30, SI60 and SI180 (CNR&SNR) and lower ADC values in active vs. inactive groups (all p<0.05) without significant difference in number or size. Strong correlations were demonstrated between abnormal segments and regional LNs in active group in terms of SI30, SI60, SI180, SOE0-30 and ADC values (r = 0.679 to 0.774, all p<0.001). SI180, SOE60-180 and ADC values were moderately correlated between abnormal segments and regional LNs in inactive group (r = 0.448 to 0.595, all p<0.05). In logistic regression analyses, SOE0-30 and ADC value of regional LNs independently predicted active CD. CONCLUSION: Mesenteric LNs follow quantitative enhancement and diffusion parameters of bowel in active CD. SOE0-30 and ADC value of LN could predict disease activity. KEY POINTS: • Mesenteric LNs may strongly follow enhancement pattern of bowel in active CD. • DWI parameters of LNs and bowel were strongly correlated in active CD. • SI180 was moderately correlated between bowel and LNs in inactive CD. • DWI parameters were moderately correlated between LNs and bowel in inactive CD. • SOE0-30 and ADC value of mesenteric LN could predict disease activity.