Endoscopic Therapy for Complete Pancreatic Ductal Obstruction in a Child With Hereditary Pancreatitis

Abstract Background and study aims Although the majority of patients with pancreas divisum (PDiv) are asymptomatic, a subgroup present with recurrent pancreatitis or pain for which endoscopic therapy may be indicated. The aim of this study was to evaluate success rates and long-term outcomes of endoscopic treatment in patients with symptomatic PDiv. Patients and methods A multicenter, retrospective cohort study was performed. Patients with symptomatic PDiv presenting with recurrent acute pancreatitis (RAP), chronic pancreatitis (CP), or chronic abdominal pancreatic-type pain (CAP) who underwent endoscopic retrograde cholangiopancreatography (ERCP) between January 2000 and December 2019 were included. The primary outcome was clinical success, defined as either no recurrent episode of acute pancreatitis (AP) for RAP patients, no flares for CP patients, or absence of abdominal pain for patients with CAP after technically successful ERCP. Results In 60 of 81 patients (74.1 %) a technically successful papilla minor intervention was performed. Adverse events were reported in 30 patients (37 %), with post-ERCP pancreatitis in 18 patients. The clinical success rate for patients with at least 3 months of follow-up was 42.6 %, with higher rates of success among patients presenting with RAP (44.4 %) as compared to those with CP (33.3 %) or CAP (33.3 %). Long-term sustained response was present in 40.9 % of patients with a technically successful intervention. In patients with RAP who did not completely respond to treatment, the mean number of AP episodes after treatment decreased significantly from 3.5 to 1.1 per year, and subsequently the interval between AP episodes increased from 278 to 690 days (P = 0.0006). A potential predictive factor of failure of clinical success after technically successful ERCP, at univariate analysis, was male sex (OR = 0.25, P = 0.02). Conclusions Endoscopic therapy in patients with symptomatic PDiv is moderately effective, with its highest yield in patients presenting with RAP. Future studies are needed to assess factors predictive for success of endoscopic therapy and potential risk factors for relapse after ERCP.

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Metastasis Prevalence and Survival of Patients with T1-2 Gastric Neuroendocrine Tumor Treated with Endoscopic Therapy and Surgery

Digestive Diseases and Sciences ◽

10.1007/s10620-021-07143-7 ◽

2021 ◽

Author(s):

Mojin Wang ◽

Shubang Cheng ◽

Lili Zhu ◽

Tao Xu ◽

Jinglin Zhang ◽

...

Keyword(s):

Neuroendocrine Tumor ◽

Endoscopic Therapy

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Performance Comparison of the Deep Learning and the Human Endoscopist for Bleeding Peptic Ulcer Disease

Journal of Medical and Biological Engineering ◽

10.1007/s40846-021-00608-0 ◽

2021 ◽

Author(s):

Hsu-Heng Yen ◽

Ping-Yu Wu ◽

Pei-Yuan Su ◽

Chia-Wei Yang ◽

Yang-Yuan Chen ◽

...

Keyword(s):

Peptic Ulcer ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Endoscopic Therapy ◽

Learning Model ◽

Ulcer Bleeding ◽

Learning Models ◽

Peptic Ulcer Bleeding ◽

Ulcer Disease ◽

Deep Learning Model

Abstract Purpose Management of peptic ulcer bleeding is clinically challenging. Accurate characterization of the bleeding during endoscopy is key for endoscopic therapy. This study aimed to assess whether a deep learning model can aid in the classification of bleeding peptic ulcer disease. Methods Endoscopic still images of patients (n = 1694) with peptic ulcer bleeding for the last 5 years were retrieved and reviewed. Overall, 2289 images were collected for deep learning model training, and 449 images were validated for the performance test. Two expert endoscopists classified the images into different classes based on their appearance. Four deep learning models, including Mobile Net V2, VGG16, Inception V4, and ResNet50, were proposed and pre-trained by ImageNet with the established convolutional neural network algorithm. A comparison of the endoscopists and trained deep learning model was performed to evaluate the model’s performance on a dataset of 449 testing images. Results The results first presented the performance comparisons of four deep learning models. The Mobile Net V2 presented the optimal performance of the proposal models. The Mobile Net V2 was chosen for further comparing the performance with the diagnostic results obtained by one senior and one novice endoscopists. The sensitivity and specificity were acceptable for the prediction of “normal” lesions in both 3-class and 4-class classifications. For the 3-class category, the sensitivity and specificity were 94.83% and 92.36%, respectively. For the 4-class category, the sensitivity and specificity were 95.40% and 92.70%, respectively. The interobserver agreement of the testing dataset of the model was moderate to substantial with the senior endoscopist. The accuracy of the determination of endoscopic therapy required and high-risk endoscopic therapy of the deep learning model was higher than that of the novice endoscopist. Conclusions In this study, the deep learning model performed better than inexperienced endoscopists. Further improvement of the model may aid in clinical decision-making during clinical practice, especially for trainee endoscopist.

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