The Use of Oblique-viewing Endoscopic Ultrasound for Accessing the Afferent Limb for Endoscopic Ultrasound-guided Biliary Drainage in Patients with Severe Stenotic Hepaticojejunal Anastomosis: One Case and Literature Review.

BACKGROUND: The treatment of hepaticojejunal anastomotic strictures in patients with surgically altered anastomosis is challenging. Endoscopic ultrasound (EUS)-guided biliary drainage is being established as a feasible biliary drainage procedure. How can oblique-viewing endoscopic ultrasound (OV-EUS) safely reach the treatment area in the afferent limb for EUS-guided hepaticojejunostomy? This is a key, meaningful, and challenging question. METHODS: A unique case of an OV-EUS-guided hepaticojejunostomy performed in a patient with severe stenotic hepaticojejunal anastomosis was reported, and the relevant literatures were reviewed. RESULTS: There are only 3 previous case reports of EUS-guided transanastomotic drainage using OV-EUS. The above 3 cases reported did not elaborate on the key treatment details of the procedure. Especially how can the OV-EUS safely reach the treatment area in the afferent limb? CONCLUSIONS: For patients with severe anastomotic stricture, when the retrograde or antegrade guide wire cannot pass through the stenosis to establish biliary drainage, OV-EUS can safely reach the treatment area in the afferent limb under the guidance of a fluoroscopic view and a guide wire. Thus, an OV-EUS-guided hepaticojejunostomy can be achieved.

Impact of simethicone on bowel cleansing during colonoscopy in Chinese patients.

BACKGROUND: Four-liter polyethylene glycol (PEG) solutions are effective for bowel cleansing, but their large volume might hinder patient compliance. Due to the unique features of Asians, 4 L PEG might be a suboptimal bowel preparation in predominantly ethnically Asian countries. In view of this, a balance should be achieved between the volume and effectiveness. The ideal bowel cleansing regimen for a colonoscopy has yet to be determined in a Chinese population. AIM: To compare the cleansing efficacy of 3 L PEG plus simethicone with 4 L PEG. METHODS: A total of 291 patients were randomly allocated to two groups: Group 1 (n = 145) received 4 L split-dose PEG (4-P); group 2 (n = 146) received 3 L split-dose PEG plus simethicone (3-PS). Bowel-cleansing efficacy was evaluated by endoscopists using the Boston bowel preparation scale (BBPS) and the bubbles score. RESULTS: Although there were no significant differences in the total BBPS score or the adequate rate of bowel preparation between the two groups, the BBPS score of the right-side colon was significantly higher in the 3-SP group (2.37 ± 0.54 vs 2.21 ± 0.78; P = 0.04). Moreover, the use of simethicone significantly reduced bubbles in all colon segments (P < 0.001). The mean withdrawal time was significantly shorter in the 3-PS group (8.8 ± 3.4 vs 9.6 ± 2.3; P = 0.02). Furthermore, significantly more proximal adenomas were detected in the 3-PS group (53.6% vs 45.7%; P = 0.03). In addition, the proportions of patients with nausea and bloating were significantly lower in the 3-SP group (P < 0.01 for both). More patients in the 3-PS group expressed willingness to repeat the bowel preparation (87.7% vs 76.6%, P = 0.01). CONCLUSION: Three-liter PEG shows satisfactory bowel cleansing despite the decrease in dosage, and addition of simethicone with better bubble elimination and enhanced patient acceptance offers excellent potential impact on the detection of proximal adenomas in Chinese patients.

Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.

BACKGROUND: Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. METHODS: 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. RESULTS: The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. CONCLUSION: Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.

Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma.

BACKGROUND: Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear shapes, texture, orientation, and tumor architecture on digital images of hematoxylin and eosin stained tissue, could help to predict recurrence in INGA patients. METHODS: A tissue microarrays cohort of 160 retrospectively INGA cases were digitally scanned, and randomly selected as training cohort (D1 = 60), validation cohort (D2 = 100 and D3 = 100, D2 and D3 are different tumor TMA spots from the same patient), accompanied with immunohistochemistry data cohort (D3' = 100, a duplicate cohort of D3) and negative controls data cohort (D5 = 100, normal adjacent tissues). After nuclear segmentation by watershed-based method, 189 local nuclear features were captured on each TMA core and the top 5 features were selected by Wilcoxon rank sum test within D1. A morphometric-based image classifier (NGAHIC) was composed across the discriminative features and predicted the recurrence in INGA on D2. The intra-tumor heterogeneity was assessed on D3. Manual nuclear atypia grading was conducted on D1 and D2 by two pathologists. The expression of HER2 and Ki67 were detected by immunohistochemistry on D3 and D3', respectively. The association between manual grading and INGA outcome was analysis. RESULTS: Independent validation results showed the NGAHIC achieved an AUC of 0.76 for recurrence prediction. NGAHIC-positive patients had poorer overall survival (P = 0.017) by univariate survival analysis. Multivariate survival analysis, controlling for T-stage, histology stage, invasion depth, demonstrated NGAHIC-positive was a reproducible prognostic factor for poorer disease-specific survival (HR = 17.24, 95% CI 3.93-75.60, P < 0.001). In contrast, human grading was only prognostic for one reader on D2. Moreover, significant correlations were observed between NGAHIC-positive patients and positivity of HER2 and Ki67 labeling index. CONCLUSIONS: The NGAHIC could provide precision oncology, personalized cancer management.