Training in Endoscopy: Endoscopic Ultrasound

Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer

OBJECTIVES

To develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer.

METHODS

A total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed.

RESULTS

A total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models.

CONCLUSIONS

A novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.

Curriculum for diagnostic endoscopic ultrasound training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement

The European Society of Gastrointestinal Endoscopy (ESGE) has recognized the need to formalize and enhance training in diagnostic endoscopic ultrasound (EUS). This manuscript represents the outcome of a formal Delphi process resulting in an official Position Statement of the ESGE and provides a framework to develop and maintain skills in diagnostic EUS. This curriculum is set out in terms of the prerequisites prior to training; the recommended steps of training to a defined syllabus; the quality of training; and how competence should be defined and evidenced before independent practice. 1:  Trainees should have achieved competence in upper gastrointestinal endoscopy before training in diagnostic EUS. 2:  The development of diagnostic EUS skills by methods that do not involve patients is advisable, but not mandatory, prior to commencing formal training in diagnostic EUS. 3:  A trainee's principal trainer should be performing adequate volumes of diagnostic EUSs to demonstrate maintenance of their own competence. 4:  Training centers for diagnostic EUS should offer expertise, as well as a high volume of procedures per year, to ensure an optimal level of quality for training. Under these conditions, training centers should be able to provide trainees with a sufficient wealth of experience in diagnostic EUS for at least 12 months. 5:  Trainees should engage in formal training and supplement this with a range of learning resources for diagnostic EUS, including EUS-guided fine-needle aspiration and biopsy (FNA/FNB). 6:  EUS training should follow a structured syllabus to guide the learning program. 7:  A minimum procedure volume should be offered to trainees during diagnostic EUS training to ensure that they have the opportunity to achieve competence in the technique. To evaluate competence in diagnostic EUS, trainees should have completed a minimum of 250 supervised EUS procedures: 80 for luminal tumors, 20 for subepithelial lesions, and 150 for pancreaticobiliary lesions. At least 75 EUS-FNA/FNBs should be performed, including mostly pancreaticobiliary lesions. 8:  Competence assessment in diagnostic EUS should take into consideration not only technical skills, but also cognitive and integrative skills. A reliable valid assessment tool should be used regularly during diagnostic EUS training to track the acquisition of competence and to support trainee feedback. 9:  A period of supervised practice should follow the start of independent activity. Supervision can be delivered either on site if other colleagues are already practicing EUS or by maintaining contacts with the training center and/or other EUS experts. 10:  Key performance measures including the annual number of procedures, frequency of obtaining a diagnostic sample during EUS-FNA/FNB, and adverse events should be recorded within an electronic documentation system and evaluated.

Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos)

BACKGROUND AND AIMS

EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object detection. We developed 2 EUS-based CNN models for normal anatomic structure recognition during real-time linear- and radial-array EUS evaluations.

METHODS

The study was performed from February 2020 to June 2022. Consecutive patient videos of linear- and radial-array EUS videos were recorded. Expert endosonographers identified and labeled 20 normal anatomic structures within the videos for training and validation of the CNN models. Initial CNN models (CNNv1) were developed from 45 videos and the improved models (CNNv2) from an additional 102 videos. CNN model performance was compared with that of 2 expert endosonographers.

RESULTS

CNNv1 used 45,034 linear-array EUS frames and 21,063 radial-array EUS frames. CNNv2 used 148,980 linear-array EUS frames and 128,871 radial-array EUS frames. Linear-array CNNv1 and radial-array CNNv1 achieved a 75.65% and 71.36% mean average precision (mAP) with a total loss of .19 and .18, respectively. Linear-array CNNv2 obtained an 88.7% mAP with a .06 total loss, whereas radial-array CNNv2 achieved an 83.5% mAP with a .07 total loss. CNNv2 accurately detected all studied normal anatomic structures with a >98% observed agreement during clinical validation.

CONCLUSIONS

The proposed CNN models accurately recognize the normal anatomic structures in prerecorded videos and real-time EUS. Prospective trials are needed to evaluate the impact of these models on the learning curves of EUS trainees.

UK and Ireland Joint Advisory Group (JAG) consensus statements for training and certification in diagnostic endoscopic ultrasound (EUS)

BACKGROUND AND AIMS

International endoscopy societies vary in their approach for credentialing individuals in endoscopic ultrasound (EUS) to enable independent practice; however, there is no consensus in this or its implementation. In 2019, the Joint Advisory Group on GI Endoscopy (JAG) commissioned a working group to examine the evidence relating to this process for EUS. The aim of this was to develop evidence-based recommendations for EUS training and certification in the UK.

METHODS

Under the oversight of the JAG quality assurance team, a modified Delphi process was conducted which included major stakeholders from the UK and Ireland. A formal literature review was made, initial questions for study were proposed and recommendations for training and certification in EUS were formulated after a rigorous assessment using the Grading of Recommendation Assessment, Development and Evaluation tool and subjected to electronic voting to identify accepted statements. These were peer reviewed by JAG and relevant stakeholder societies before consensus on the final EUS certification pathway was achieved.

RESULTS

39 initial questions were proposed of which 33 were deemed worthy of assessment and finally formed the key recommendations. The statements covered four key domains, such as: definition of competence (13 statements), acquisition of competence (10), assessment of competence (5) and postcertification mentorship (5). Key recommendations include: (1) minimum of 250 hands-on cases before an assessment for competency can be made, (2) attendance at the JAG basic EUS course, (3) completing a minimum of one formative direct observation of procedural skills (DOPS) every 10 cases to allow the learning curve in EUS training to be adequately studied, (4) competent performance in summative DOPS assessments and (5) a period of mentorship over a 12-month period is recommended as minimum to support and mentor new service providers.

CONCLUSIONS

An evidence-based certification pathway has been commissioned by JAG to support and quality assure EUS training. This will form the basis to improve quality of training and safety standards in EUS in the UK and Ireland.

Analysis of the variation in learning curves for achieving competency in convex EUS training: a prospective cohort study using a standardized assessment tool

BACKGROUND AND AIMS

The need for mastering standard imaging techniques for convex EUS in the biliopancreatic regions has been increasing; however, large variations in the aptitude for achieving EUS competency hinder expert development. Therefore, we investigated the factors influencing the achievement of expert competency in EUS using a new assessment tool for multiple imaging items.

METHODS

Between January 2018 and February 2022, 3277 consecutive EUS procedures conducted by 5 beginners (EUS procedures <250), 7 intermediate trainees (250-749), and 2 experts (≥750) were prospectively evaluated. Immediately after each EUS procedure, the success or failure of imaging for each item was recorded using a newly developed EUS assessment tool that requires 17 items to be photographed. After correcting for missing values using multiple imputation, learning curves of EUS scores were created, and a competency was set based on expert scores. Finally, a comparative analysis between high and low performers was performed to extract factors influencing EUS scores.

RESULTS

Although 3 of 7 intermediates (43%; mean, 317 cases) achieved competency, none of the beginners achieved competency. During a comparative analysis, although no significant difference in the number of EUS procedures performed was observed between the high and low performers, the former had significantly higher scores in the written test (theoretical knowledge).

CONCLUSIONS

Our results showed that theoretical knowledge, rather than the number of EUS cases, may be a possible influencing factor for distinguishing high and low performers after treating 250 cases. (Clinical trial registration number: UMIN 000043271.).

Advances in Pediatric Diagnostic Endoscopy: A State-of-the-Art Review

Pediatric endoscopy has revolutionized the way we diagnose and treat gastrointestinal disorders in children. Technological advances in computer processing and imaging continue to affect endoscopic equipment and advance diagnostic tools for pediatric endoscopy. Although commonly used by adult gastroenterologists, modalities, such as endomicroscopy, image-enhanced endoscopy, and impedance planimetry, are not routinely used in pediatric gastroenterology. This state-of-the-art review describes advances in diagnostic modalities, including image-enhanced endoscopy, confocal laser endomicroscopy, optical coherence tomography, endo functional luminal imaging probes, wireless motility/pH capsule, wireless colon capsule endoscopy, endoscopic ultrasound, and discusses the basic principles of each technology, including adult indications and pediatric applications, safety cost, and training data.

A core curriculum for basic EUS skills: An international consensus using the Delphi methodology

Background and Objectives:

Materials and Methods:

Results:

Conclusions:

Training in advanced bilio-pancreatic endoscopy

As the clinical applications of endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) take more and more root in everyday practice and these endoscopic procedures become increasingly more complex and interventional with a higher chance for complications, the demand for expert bilio-pancreatic endoscopists continues to increase. In response to this growing need, specific postgraduate fellowships have been developed, however the standardization of training programs and the assessment of the achieved competence still remains an open debate. ERCP and EUS competency often requires training beyond the scope of a standard GI fellowship program, which lasts at least 1-2 years, and there are some differences in the way to face the issues of advanced bilio-pancreatic endoscopy training between Europe, America and the Asian regions. Today there is no role for the self-teaching of ERCP and EUS through trial and error without supervision and, in the near future, it is necessary to critically revise current training guidelines, to establish a standardized curriculum for advanced bilio-pancreatic endoscopists and to implement universally validated skill assessment tools, able to ensure constant and targeted feedback to trainees.

Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video)

BACKGROUND AND AIMS

EUS is considered one of the most sensitive modalities for pancreatic cancer detection, but it is highly operator-dependent and the learning curve is steep. In this study, we constructed a system named BP MASTER (pancreaticobiliary master) for EUS training and quality control.

METHODS

The standard procedure of pancreatic EUS was divided into 6 stations. We developed a station classification model and a pancreas/abdominal aorta/portal confluence segmentation model with 19,486 images and 2207 images, respectively. Then, we used 1920 images and 700 images for classification and segmentation internal validation, respectively. To test station recognition we used 396 videos clips. An independent data set containing 180 images was applied for comparing the performance between models and EUS experts. Seven hundred sixty-eight images from 2 other hospitals were used for external validation. A crossover study was conducted to test the system effect on reducing difficulty in ultrasonographics interpretation among trainees.

RESULTS

The models achieved 94.2% accuracy in station classification and .836 dice in segmentation at internal validation. At external validation, the models achieved 82.4% accuracy in station classification and .715 dice in segmentation. For the video test, the station classification model achieved a per-frame accuracy of 86.2%. Compared with EUS experts, the models achieved 90.0% accuracy in classification and .77 and .813 dice in blood vessel and pancreas segmentation, which is comparable with that of experts. In the crossover study, trainee station recognition accuracy improved from 67.2% to 78.4% (95% confidence interval, .058-1.663; P < .01).

CONCLUSIONS

The BP MASTER system has the potential to play an important role in shortening the pancreatic EUS learning curve and improving EUS quality control in the future.

Exploration of an effective training system for the diagnosis of pancreatobiliary diseases with EUS: A prospective study

BACKGROUND AND OBJECTIVE

There are limited data on multistage-based training programs focused on EUS. We aimed to explore an effective training system for diagnosing pancreaticobiliary diseases with EUS.

MATERIALS AND METHODS

Nine advanced endoscopy trainees (AETs) with less EUS experience from nine institutions were recruited. The training system consisted of multiple stages and multi-teaching methods, including biliopancreatic standard scanning, anatomy and imaging knowledge, simulator, hands-on operations, error correction, and case analysis over a 12-month training period. Grading for technical and cognitive skills was assessed using The EUS Skills Assessment Tool.

RESULTS

After training, the overall scores for radial (4.16 ± 0.21 vs. 1.46 ± 0.16, P < 0.01) and linear (4.43 ± 0.20 vs. 1.63 ± 0.23, P < 0.01) scanning were significantly improved. The aortopulmonary window/mediastinum station can be learned more easily by AETs compared with other stations (P = 0023). The scanning of the descending part of the duodenum seemed to improve the slowest after training (P = 0.0072), indicating that the descending part of the duodenum can be more difficult and should be the focus of training. Every teaching method heightened EUS competence, especially case analysis and hands-on operations. AETs achieved equivalent EUS competence after training despite their initial experience. Through a poststudy questionnaire, it was found that all AETs strongly agreed they were satisfied with the training system, and their confidence was greatly enhanced when EUS was performed independently.

CONCLUSIONS

The current multistage and multi-methods training system showed efficient performance in the cognitive and technical competence of EUS. Descending part of duodenum scanning was difficult for beginners and should be the focus of training.

Ingestible Sensors and Sensing Systems for Minimally Invasive Diagnosis and Monitoring: The Next Frontier in Minimally Invasive Screening

Ingestible electronic systems that are capable of embedded sensing, particularly within the gastrointestinal (GI) tract and its accessory organs, have the potential to screen for diseases that are difficult if not impossible to detect at an early stage using other means. Furthermore, these devices have the potential to (1) reduce labor and facility costs for a variety of procedures, (2) promote research for discovering new biomarker targets for associated pathologies, (3) promote the development of autonomous or semiautonomous diagnostic aids for consumers, and (4) provide a foundation for epithelially targeted therapeutic interventions. These technological advances have the potential to make disease surveillance and treatment far more effective for a variety of conditions, allowing patients to lead longer and more productive lives. This review will examine the conventional techniques, as well as ingestible sensors and sensing systems that are currently under development for use in disease screening and diagnosis for GI disorders. Design considerations, fabrication, and applications will be discussed.

Impact of balloon inflation on the insertion of endoscopic ultrasound: a prospective, randomized controlled trial

Background and study aims  During endoscopic ultrasound (EUS), patients may experience severe discomfort. The radial echoendoscope has a balloon around its tip. Balloon inflation prior to insertion may reduce contact injury and pharyngeal pain. The purpose of this study was to investigate the effect of balloon inflation on pharyngeal pain during insertion. Patients and methods  Patients who underwent radial EUS for pancreatobiliary disease were randomized into standard insertion or balloon-inflated insertion. The primary outcome was the proportion of moderate-to-severe pharyngeal pain. Secondary outcomes were the degree of pharyngeal pain, risk factors for moderate pharyngeal pain, procedure-related adverse events, and pharyngeal pain depending on the experience of the endoscopist. Results  A total of 481 patients were randomized into two groups: standard insertion (238) and balloon inflation (243). No statistically significant differences in proportion of moderate-to-severe pain were found (26.5 % vs. 20.2 %, P  = 0.107). Balloon inflation (HR 0.65; 95 % CI (0.42-0.98, P  = 0.041) was a protective factor against moderate pain. Balloon inflation reduced the proportion of patients with moderate-to-severe pain when performed by physicians with less than 3months of experience with EUS (44.7 % vs. 25.3 %, P  = 0.012). Conclusion  Balloon inflation did not reduce the absolute degree of post-procedural pain with EUS, but it reduced the number of patients with moderate-to-severe pain when performed by physicians with less than 3 months of experience.

EUS anatomy of the pancreatobiliary system in a swine model: The WISE experience

Background and Objectives

EUS training is recognized to have a substantial learning curve. To date, few dedicated training programs for EUS have been described. The swine model has been highlighted as a realistic tool to enhance EUS training. Studies extensively describing EUS swine anatomy are lacking in the current literature. The article aims to describe both radial and linear EUS pancreatobiliary swine anatomy.

Materials and Methods

Four live pigs were endoscoped under general anesthesia using both radial and linear array echoendoscopes. Relevant images and videos were recorded.

Results

It was possible to effectively image aorta, crus of the diaphragm, celiac trunk, superior mesenteric artery, pancreas, common bile duct, gallbladder, portal vein, kidneys, spleen, and hepatic hilum. Images were comparable to human EUS findings, with some remarkable differences. The pancreas was relatively larger in swine and in contrast to humans has three segments (duodenal, splenic, and connecting lobe).

Conclusions

The swine model was a highly realistic teaching model for linear and radial pancreatobiliary EUS and a useful tool for training in the setting of in vivo hands-on sessions.