Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos)

Tissue harmonic versus contrast-enhanced harmonic endoscopic ultrasonography for the diagnosis of pancreatic tumors: Prospective multicenter study

OBJECTIVES

This prospective multicenter study aimed to assess and compare the accuracy of tissue harmonic endoscopic ultrasonography (TH-EUS) and contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) for differentiating pancreatic carcinoma from other pancreatic tumors.

METHODS

Consecutive patients with solid pancreatic tumors were prospectively enrolled between August 2013 and December 2014. To assess the accuracy of TH-EUS and CH-EUS, we compared four parameters of TH-EUS (fuzzy edge, irregular periphery, hypoechogenicity, and heterogeneous internal echogenicity) and four parameters of CH-EUS (hypoenhancement and heterogeneous enhancement in the early and late phases, respectively) to investigate which parameter of each method was most suitable to diagnose pancreatic carcinomas. Interobserver agreement and the diagnostic ability of pancreatic carcinoma using TH-EUS and CH-EUS were assessed and compared.

RESULTS

A total of 204 patients were enrolled. For the diagnosis of pancreatic carcinoma, interobserver agreement by experts and nonexperts was 0.33-0.50 and 0.35-0.50 for TH-EUS, respectively, and 0.72-0.74 and 0.20-0.54 for CH-EUS, respectively. Irregular periphery was the most accurate diagnostic parameter among TH-EUS findings for differentiating pancreatic carcinomas, with sensitivity, specificity, and accuracy of 95.0%, 42.9%, and 78.9%, respectively. Late phase hypoenhancement was the most accurate diagnostic parameter among CH-EUS findings for differentiating pancreatic carcinomas, with sensitivity, specificity, and accuracy of 90.8%, 74.6%, and 85.8%, respectively. The accuracy of CH-EUS (late phase hypoenhancement) for diagnosis of pancreatic carcinoma was significantly higher than that of TH-EUS (irregular periphery) (p < 0.001).

CONCLUSION

In comparison with TH-EUS, CH-EUS increased the diagnostic ability and reproducibility for the diagnosis of pancreatic carcinoma. UMIN (000011124).

Application of artificial intelligence to pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVES

Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.

METHODS

We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.

RESULTS

Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.

CONCLUSIONS

AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.

Application of artificial intelligence for diagnosis of pancreatic ductal adenocarcinoma by EUS: A systematic review and meta-analysis

EUS-guided tissue acquisition carries certain risks from unnecessary needle puncture in the low-likelihood lesions. Artificial intelligence (AI) system may enable us to resolve these limitations. We aimed to assess the performance of AI-assisted diagnosis of pancreatic ductal adenocarcinoma (PDAC) by off-line evaluating the EUS images from different modes. The databases PubMed, EMBASE, SCOPUS, ISI, IEEE, and Association for Computing Machinery were systematically searched for relevant studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic curve were estimated using R software. Of 369 publications, 8 studies with a total of 870 PDAC patients were included. The pooled sensitivity and specificity of AI-assisted EUS were 0.91 (95% confidence interval [CI], 0.87-0.93) and 0.90 (95% CI, 0.79-0.96), respectively, with DOR of 81.6 (95% CI, 32.2-207.3), for diagnosis of PDAC. The area under the curve was 0.923. AI-assisted B-mode EUS had pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.91, 0.90, 0.94, and 0.84, respectively; while AI-assisted contrast-enhanced EUS and AI-assisted EUS elastography had sensitivity, specificity, PPV, and NPV of 0.95, 0.95, 0.97, and 0.90; and 0.88, 0.83, 0.96 and 0.57, respectively. AI-assisted EUS has a high accuracy rate and may potentially enhance the performance of EUS by aiding the endosonographers to distinguish PDAC from other solid lesions. Validation of these findings in other independent cohorts and improvement of AI function as a real-time diagnosis to guide for tissue acquisition are warranted.

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.

Scope of Artificial Intelligence in Gastrointestinal Oncology

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

The Asian Federation of Societies for Ultrasound in Medicine and Biology (AFSUMB) Guidelines for Contrast-Enhanced Endoscopic Ultrasound

The Asian Federation of Societies for Ultrasound in Medicine and Biology aimed to provide information on techniques and indications for contrast-enhanced harmonic endoscopic ultrasound (CH-EUS), and to create statements including the level of recommendation. These statements are based on current scientific evidence reviewed by a Consensus Panel of 15 internationally renowned experts. The reliability of clinical questions was measured by agreement rates after voting. Six statements were made on techniques, including suitable contrast agents for CH-EUS, differences between contrast agents, setting of mechanical index, dual imaging and duration and phases for observation. Thirteen statements were made on indications, including pancreatic solid masses, pancreatic cancer staging, pancreatic cystic lesions and mural nodules, detection of subtle pancreatic lesions, gallbladder sludge and polyps, hepatic lesions, lymph nodes, subepithelial lesions, visceral vascular diseases, guidance of fine needle aspiration and evaluation for local therapy. These international expert consensus guidelines will assist endosonographers in conducting CH-EUS according to evidence-based information.

Artificial intelligence for pancreatic cancer detection: Recent development and future direction

Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.

Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising

Pancreatic ductal adenocarcinoma (PDAC) is a worldwide public health concern. Despite extensive research efforts toward improving diagnosis and treatment, the 5-year survival rate at best is approximately 15%. This dismal figure can be attributed to a variety of factors including lack of adequate screening methods, late symptom onset, and treatment resistance. Pancreatic ductal adenocarcinoma remains a grim diagnosis with a high mortality rate and a significant psy-chological burden for patients and their families. In recent years artificial intelligence (AI) has permeated the medical field at an accelerated pace, bringing potential new tools that carry the promise of improving diagnosis and treatment of a variety of diseases. In this review we will summarize the landscape of AI in diagnosis and treatment of PDAC.

Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases

There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.

Role of Endoscopic Ultrasound in the Diagnosis of Pancreatic Neuroendocrine Neoplasms

Although pancreatic neuroendocrine neoplasms (PNENs) are relatively rare tumors, their number is increasing with advances in diagnostic imaging modalities. Even small lesions that are difficult to detect using computed tomography or magnetic resonance imaging can now be detected with endoscopic ultrasound (EUS). Contrast-enhanced EUS is useful, and not only diagnosis but also malignancy detection has become possible by evaluating the vascularity of tumors. Pathological diagnosis using EUS with fine-needle aspiration (EUS-FNA) is useful when diagnostic imaging is difficult. EUS-FNA can also be used to evaluate the grade of malignancy. Pooling the data of the studies that compared the PNENs grading between EUS-FNA samples and surgical specimens showed a concordance rate of 77.5% (κ-statistic = 0.65, 95% confidence interval = 0.59–0.71, p < 0.01). Stratified analysis for small tumor size (2 cm) showed that the concordance rate was 84.5% and the kappa correlation index was 0.59 (95% confidence interval = 0.43–0.74, p < 0.01). The evolution of ultrasound imaging technologies such as contrast-enhanced and elastography and the artificial intelligence that analyzes them, the evolution of needles, and genetic analysis, will further develop the diagnosis and treatment of PNENs in the future.

Role of Endoscopic Ultrasonography and Endoscopic Retrograde Cholangiopancreatography in the Diagnosis of Pancreatic Cancer

Pancreatic cancer has the poorest prognosis among all cancers, and early diagnosis is essential for improving the prognosis. Along with radiologic modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), endoscopic modalities play an important role in the diagnosis of pancreatic cancer. This review evaluates the roles of two of those modalities, endoscopic ultrasonography (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), in the diagnosis of pancreatic cancer. EUS can detect pancreatic cancer with higher sensitivity and has excellent sensitivity for the diagnosis of small pancreatic cancer that cannot be detected by other imaging modalities. EUS may be useful for the surveillance of pancreatic cancer in high-risk individuals. Contrast-enhanced EUS and EUS elastography are also useful for differentiating solid pancreatic tumors. In addition, EUS-guided fine needle aspiration shows excellent sensitivity and specificity, even for small pancreatic cancer, and is an essential examination method for the definitive pathological diagnosis and treatment decision strategy. On the other hand, ERCP is invasive and performed less frequently for the purpose of diagnosing pancreatic cancer. However, ERCP is essential in cases that require evaluation of pancreatic duct stricture that may be early pancreatic cancer or those that require differentiation from focal autoimmune pancreatitis.

Enhanced EUS imaging (with videos)

BACKGROUND AND AIMS

EUS remains a primary diagnostic tool for the evaluation of pancreaticobiliary disease. Although EUS combined with FNA or biopsy sampling is highly sensitive for the diagnosis of neoplasia within the pancreaticobiliary tract, limitations exist in specific clinical settings such as chronic pancreatitis. Enhanced EUS imaging technologies aim to aid in the detection and diagnosis of lesions that are commonly evaluated with EUS.

METHODS

We reviewed technologies and methods for enhanced imaging during EUS and applications of these methods. Available data regarding efficacy, safety, and financial considerations are summarized.

RESULTS

Enhanced EUS imaging methods include elastography and contrast-enhanced EUS (CE-EUS). Both technologies have been best studied in the setting of pancreatic mass lesions. Robust data indicate that neither technology has adequate specificity to serve as a stand-alone test for pancreatic malignancy. However, there may be a role for improving the targeting of sampling and in the evaluation of peritumoral lymph nodes, inflammatory pancreatic masses, and masses with nondiagnostic FNA or fine-needle biopsy sampling. Further, novel applications of these technologies have been reported in the evaluation of liver fibrosis, pancreatic cysts, and angiogenesis within neoplastic lesions.

CONCLUSIONS

Elastography and CE-EUS may improve the real-time evaluation of intra- and extraluminal lesions as an adjunct to standard B-mode and Doppler imaging. They are not a replacement for EUS-guided tissue sampling but provide adjunctive diagnostic information in specific clinical situations. The optimal clinical use of these technologies continues to be a focus of ongoing research.

Artificial intelligence in pancreaticobiliary endoscopy

Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobiliary disorders. AI-based methods were applied to extract information, in prognostication, to guide clinical treatment decisions and in pancreatobiliary endoscopy to characterize lesions. AI applications in endoscopy are expected to reduce inter-operator variability, improve the accuracy of diagnosis, and assist in therapeutic decision-making in real time. AI-based literature must however be interpreted with caution given the limited external validation. A multidisciplinary approach combining clinical and imaging or endoscopy data will better utilize AI-based technologies to further improve patient care.

Application of artificial intelligence in pancreaticobiliary diseases

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

Artificial intelligence for the management of pancreatic diseases

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.

Current status of artificial intelligence analysis for endoscopic ultrasonography

Endoscopic ultrasonography (EUS) is an essential diagnostic tool for various types of pancreatic diseases such as pancreatic tumors and chronic pancreatitis; however, EUS imaging has low specificity for the diagnosis of pancreatic diseases. Artificial intelligence (AI) is a mathematical prediction technique that automates learning and recognizes patterns in data. This review describes the details and principles of AI and deep learning algorithms. The term AI does not have any definite definition; almost all AI systems fall under narrow AI, which can handle single or limited tasks. Deep learning is based on neural networks, which is a machine learning technique that is widely used in the medical field. Deep learning involves three phases: data collection and annotation, building the deep learning architecture, and training and ability validation. For medical image diagnosis, image classification, object detection, and semantic segmentation are performed. In EUS, AI is used for detecting anatomical features, differential pancreatic tumors, and cysts. For this, conventional machine learning architectures are used, and deep learning architecture has been used in only two reports. Although the diagnostic abilities in these reports were about 85-95%, these were exploratory research and very few reports have included substantial evidence. AI is increasingly being used for medical image diagnosis due to its high performance and will soon become an essential technique for medical diagnosis.

Artificial intelligence in biliopancreatic endoscopy: Is there any role?

Artificial intelligence (AI) research in endoscopy is being translated at rapid pace with a number of approved devices now available for use in luminal endoscopy. However, the published literature for AI in biliopancreatic endoscopy is predominantly limited to early pre-clinical studies including applications for diagnostic EUS and patient risk stratification. Potential future use cases are highlighted in this manuscript including optical characterisation of strictures during cholangioscopy, prediction of post-ERCP acute pancreatitis and selective biliary duct cannulation difficulty, automated report generation and novel AI-based quality key performance metrics. To realise the full potential of AI and accelerate innovation, it is crucial that robust inter-disciplinary collaborations are formed between biliopancreatic endoscopists and AI researchers.

The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders

The use of artificial intelligence (AI) in various medical imaging applications has expanded remarkably, and several reports have focused on endoscopic ultrasound (EUS) images of the pancreas. This review briefly summarizes each report in order to help endoscopists better understand and utilize the potential of this rapidly developing AI, after a description of the fundamentals of the AI involved, as is necessary for understanding each study. At first, conventional computer-aided diagnosis (CAD) was used, which extracts and selects features from imaging data using various methods and introduces them into machine learning algorithms as inputs. Deep learning-based CAD utilizing convolutional neural networks has been used; in these approaches, the images themselves are used as inputs, and more information can be analyzed in less time and with higher accuracy. In the field of EUS imaging, although AI is still in its infancy, further research and development of AI applications is expected to contribute to the role of optical biopsy as an alternative to EUS-guided tissue sampling while also improving diagnostic accuracy through double reading with humans and contributing to EUS education.

Evolving role of artificial intelligence in gastrointestinal endoscopy

Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study

BACKGROUND/PURPOSE

The application of artificial intelligence to clinical diagnostics using deep learning has been developed in recent years. In this study, we developed an original computer-assisted diagnosis (CAD) system using deep learning analysis of EUS images (EUS-CAD), and assessed its ability to detect pancreatic ductal carcinoma (PDAC), using control images from patients with chronic pancreatitis (CP) and those with a normal pancreas (NP).

METHODS

A total of 920 endosonographic images were used for the training and 10-fold cross-validation, and another 470 images were independently tested. The detection abilities in both the validation and test setting were assessed, and independent factors associated with misdetection were identified among participants' characteristics and endosonographic image features.

RESULTS

Regarding the detection ability of EUS-CAD, the areas under the receiver operating characteristic curve were found to be 0.924 and 0.940 in the validation and test setting, respectively. In the analysis of misdetection, no factors were identified on univariate analysis in PDAC cases. On multivariate analysis of non-PDAC cases, only mass formation was associated with overdiagnosis of tumors.

CONCLUSIONS

Our pilot study demonstrated the efficacy of EUS-CAD for the detection of PDAC.

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.

Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic adenocarcinoma. Artificial intelligence (AI) is a mathematical concept whose implementation automates learning and recognizing data patterns. The aim of this study was to investigate whether AI via deep learning algorithms using endoscopic ultrasonography (EUS) images of IPMNs could predict malignancy.

Chronic pancreatitis: an overview of diagnosis and management

INTRODUCTION

Chronic pancreatitis entails a heavy burden on the healthcare system because of its often protracted evolution, requiring complex diagnostic and therapeutic procedures.

AREAS COVERED

This review focuses on novel imaging and endoscopic diagnostic and therapeutic interventions that have changed the management of patients with chronic pancreatitis. We have conducted an extensive search of original papers and guidelines, in order to provide a comprehensive and up to date review of available evidence in these areas of interest.

EXPERT OPINION

The traditional challenges in managing chronic pancreatitis patients stemmed from the limitations of diagnostic modalities, which could not correctly identify patients in an early stage of the disease, as well as from the scarcity of therapeutic options available. Advances in imaging of CT-scan, MRI, and EUS have opened the way for early diagnosis and staging. This has allowed more aggressive and tailored therapeutic modalities, particularly in endoscopic therapy and minimally invasive surgical interventions. Although high-quality data from large RCTs is still scarce, evidence-based algorithms for diagnosis and therapy are now changing the way we address this chronic disease. In the near future, we can expect a tailored approach based on patient and disease-related predictive factors, relying on a vast armamentarium of endoscopic and surgical solutions.

The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid

The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.

Qualitative and Quantitative Contrast-enhanced Endoscopic Ultrasound Improves Evaluation of Focal Pancreatic Lesions

BACKGROUND & AIMS

Endoscopic ultrasound (EUS) is a sensitive method to evaluate the pancreas but its diagnostic capability for several diseases is limited. We compared the diagnostic yield of unenhanced EUS with that of contrast-enhanced EUS for focal pancreas lesions and identified and tested quantitative parameters of contrast enhancement.

METHODS

We performed a prospective tandem-controlled trial in which 101 patients with focal pancreas lesions (48 with masses, 28 with cysts, and 25 with pancreatitis) underwent conventional EUS followed by contrast EUS using intravenous perflutren microspheres. The diagnosis at each stage was scored and compared with a standard (findings from surgical pathology analysis, cytologic, and/or 6-month clinical follow-up evaluations). Quantitative parameters were generated by time-intensity curve analysis. Solid lesions were divided into derivation and testing cohorts for a crossover validation analysis of the quantitative parameters. The primary outcome was diagnostic yield of unenhanced vs contrast EUS in analysis of focal pancreas lesions.

RESULTS

Contrast increased the diagnostic yield of EUS from 64% (65/101 lesions accurately assessed) to 91% (92/101 lesions accurately assessed); the odds ratio [OR] was 7.8 (95% CI, 2.7-30.2) for accurate analysis of lesions by contrast-enhanced EUS relative to unenhanced EUS. The contrast increased accuracy of analysis of pancreas masses (OR, 6.0; 95% CI, 1.8-31.8), improving assessment of neuroendocrine and other (non-carcinoma) tumors. Contrast increased the diagnostic yield for pancreas cysts to 96% (27/28) compared with 71.4% (20/28) for unenhanced EUS (P = .02), due to improved differentiation of mural nodules vs debris. Time-intensity curve analysis revealed distinct patterns of relative peak enhancement (rPE) and in-slope (rIS) for different lesions following contrast injection: for adenocarcinomas, values were low rPE and low rIS; for neuroendocrine masses, values were high rPE and normal IS; and for chronic pancreatitis foci, values were normal rPE and low rIS. In the validation cohort, these parameters correctly characterized 91% of lesions and improved yield relative to unenhanced EUS (OR, 10; 95% CI, 1.4-34.0).

CONCLUSIONS

Contrast-enhanced EUS improves the accuracy of analysis of focal pancreas lesions, compared with unenhanced EUS. Integration of practical quantitative parameters, specifically relative peak enhancement and in-slope, might increase the diagnostic accuracy of contrast EUS. ClinicalTrials.gov no: 02863770.

The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid

The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.

Do we need contrast agents for EUS?

We recently introduced a series of articles that dealt with controversies in EUS. In Part I, the authors discussed which clinical information is necessary prior to EUS and whether other imaging modalities are required before embarking on EUS examinations. Part II focuses on technical details and controversies about the use of EUS in special situations. In this article, important practical issues regarding the application of contrast-enhanced EUS in various clinical settings are raised and controversially discussed from different points of view.

EUS-guided tissue acquisition in chronic pancreatitis: Differential diagnosis between pancreatic cancer and pseudotumoral masses using EUS-FNA or core biopsy

Background and Objective:

EUS-FNA sensitivity for malignancy in parenchymal masses of patients with concurrent chronic pancreatitis (CP) has been reported to be unsatisfactory. The aim of the present study was to directly compare the diagnostic accuracy of EUS-FNA and EUS-fine-needle biopsy (FNB) in differentiating between inflammatory masses and malignancies in the setting of CP.

Methods:

We performed a retrospective analysis of prospective, multicentric databases of all patients with pancreatic masses and clinico-radiological-endosonographic features of CP who underwent EUS-FNA or FNB.

Results:

Among 1124 patients with CP, 210 patients (60% males, mean age: 62.7 years) with CP and pancreatic masses met the inclusion criteria and were enrolled. In the FNA group (110 patients), a correct diagnosis was obtained in all but 18 cases (diagnostic accuracy 83.6%, sensitivity 69.5%, specificity 100%, positive predictive value [PPV] 100%, and negative predictive value [NPV] 73.9%); by contrast, among 100 patients undergoing FNB, a correct diagnosis was obtained in all but seven cases (diagnostic accuracy 93%, sensitivity 86.8%, specificity 100%, PPV 100%, and NPV 87%) (P = 0.03, 0.03, 1, 1, and 0.07, respectively). At binary logistic regression, focal pancreatitis (odds of event occurrence [OR]: 4.9; P < 0.001), higher Ca19-9 (OR: 2.3; P = 0.02), and FNB (OR: 2.5; P < 0.01) were the only independent factors associated with a correct diagnosis.

Conclusion:

EUS-FNB is effective in the differential diagnosis between pseudotumoral masses and solid neoplasms in CP, showing higher diagnostic accuracy and sensitivity than EUS-FNA. EUS-FNB should be considered the preferred diagnostic technique for diagnosing cancer in the setting of CP.

Application of Artificial Intelligence to Gastroenterology and Hepatology

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Utility of Contrast-Enhanced Harmonic Endoscopic Ultrasonography for Early Diagnosis of Small Pancreatic Cancer

This study aimed to assess whether contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS), compared to multidetector-row computed tomography (MDCT) and magnetic resonance imaging (MRI), is useful for early diagnosis of small pancreatic cancer (PC). Between March 2010 and June 2018, all three imaging modalities and surgery were performed for patients with a pancreatic solid lesion measuring ≤20 mm; diagnostic ability was compared among modalities. Fifty-one of 60 patients were diagnosed with PC (PC size in 41 patients: 11-20 mm; 10 patients: ≤10 mm). The sensitivity, specificity, and accuracy of CH-EUS, MDCT, and MRI for PC (11-20 mm) detection were 95%/83%/94%, 78%/83%/79%, and 73%/33%/68%, respectively. The diagnostic ability of CH-EUS was significantly superior compared with MDCT and MRI (p = 0.002 and p = 0.007, respectively). The sensitivity, specificity, and accuracy of CH-EUS, MDCT, and MRI for PC (≤10 mm) detection were 70%/100%/77%, 20%/100%/38%, and 50%/100%/62%, respectively. The diagnostic ability of CH-EUS tended to be superior to that of MDCT (p = 0.025). The sensitivity of MDCT for PC (≤10 mm) detection was significantly lower than that for PC (11-20 mm) detection (20% vs. 78%; p = 0.001). CH-EUS, compared to MDCT and MRI, is useful for diagnosing small PCs.

Contrast-enhanced harmonic endoscopic ultrasonography for the differential diagnosis of pancreatic masses: A systematic review and meta-analysis

Understanding the difference between malignant and benign pancreatic masses is critical in terms of diagnosis, although this is difficult to determine in clinical practice. The contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) technique was introduced in 2010, although, to the best of the authors' knowledge, there has been no systematic review or meta-analysis to date evaluating its diagnostic performance for the differentiation of pancreatic masses. The aim of the present study was to systematically evaluate the diagnostic performance of CH-EUS for the differentiation of pancreatic masses. Search key words and inclusion and exclusion criteria were initially presented. Two independent authors read and extracted the relevant information from the included studies. Disagreements were resolved through discussion with another two experienced authors. Metadisc and Stata software were used for the meta-analysis and the evaluation of bias. A total of 16 studies comprising 1,325 patients were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio of CH-EUS were used to distinguish between malignant and benign tumors, and the values obtained were 93% [95% confidence interval (CI): 91-94%], 84% (95% CI: 80-87%), 5.58 (95% CI: 3.90-7.97), 0.09 (95% CI: 0.07-0.11) and 72.56 (95% CI: 48.93-107.60), respectively. The area under the summary receiver operating characteristic curve was determined to be 0.96. No publication bias was identified in this meta-analysis. Taken together, these results confirm that CH-EUS has a high accuracy rate for distinguishing between benign and malignant pancreatic space-occupying lesions, and it may therefore be used as an effective diagnostic tool for pancreatic masses.

Value of contrast-enhanced harmonic endoscopic ultrasonography with enhancement pattern for diagnosis of pancreatic cancer: A meta-analysis

BACKGROUND

Current imaging modalities are limited in their ability to distinguish pancreatic cancer (PC) from non-neoplastic pancreatic lesions. The diagnostic use of contrast-enhanced endoscopic ultrasonography (CE-EUS) has increased, and its utility has been reported. Recently, contrast-enhanced harmonic EUS (CH-EUS) was reported to facilitate imaging of parenchymal perfusion and microvessels in pancreatobiliary diseases, leading to a high diagnostic accuracy for PC. The present meta-analysis aims to investigate the usefulness of CH-EUS with enhancement pattern for PC diagnosis.

METHODS

A systematic meta-analysis of all potentially relevant articles identified in PubMed, the Cochrane library, and Medline was carried out. Fixed-effects or random-effects models were used to investigate pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio with 95% confidence interval (CI).

RESULTS

The study enrolled 887 patients from nine eligible studies. Pooled estimates of sensitivity and specificity were 93% (95% CI, 0.91-0.95) and 80% (95% CI, 0.75-0.85), respectively. Subgroup analyses were carried out on the main results after excluding two outliers. Area under summary receiver operating characteristics curve was 0.97. No publication bias was found using funnel plots. No significant relationship was found between the diagnostic odds ratios and the characteristics of the studies including continent and contrast agent.

CONCLUSIONS

This meta-analysis showed that CH-EUS with qualitative analysis of enhancement pattern is useful for the diagnosis of PC, and has high sensitivity and accuracy, regardless of the type of contrast agent used. This modality may provide improved diagnostic accuracy for PC in clinical practice.

Instant evaluation of contrast enhanced endoscopic ultrasound helps to differentiate various solid pancreatic lesions in daily routine

BACKGROUND

Contrast enhanced harmonic endoscopic ultrasound (CEH-EUS) is a spreading technique; some studies have shown its value in the diagnosis of pancreatic adenocarcinoma using quantitative analysis.

AIM

To examine the value of CEH-EUS for differentiating various pancreatic lesions in everyday routine with qualitative and quantitative analysis.

METHODS

Data of 55 patients with pancreatic lesions who underwent CEH-EUS were analysed retrospectively. Perfusion characteristics were classified by the investigator qualitatively immediately upon investigation, quantitative analysis was performed later on. Samples from fine needle aspiration (EUS-FNA) or surgical specimen served as gold standard.

RESULTS

CEH-EUS showed 39 hypoenhanced lesions, 3 non-enhanced and 13 hyperenhanced lesions. Concordance of the investigators qualitative classification of peak contrast enhancement with quantitative analysis later on was 100%, while other parameters such as arrival time, time to peak or area under the curve did not show additional value. 34 of 39 hypoenhanced lesions were pancreatic adenocarcinoma; of the hyperenhanced lesions 4 were inflammatory, 3 neuroendocrine carcinomas, 1 lymphoma, 1 insulinoma and 4 metastases (2 of renal cell carcinoma, 2 of lung cancer). Non-enhanced lesions showed up as necroses. Sensitivity for the detection of pancreatic adenocarcinoma was 100%, specificity 87.2% for hypoenhancement alone; in otherwise healthy pancreatic tissue all hypoenhanced lesions were pancreatic adenocarcinoma (sensitivity and specificity 100%, PPV and NPV for adenocarcinoma 100%).

CONCLUSION

This study again shows the excellent value of CEH-EUS in everyday routine for diagnostics of various focal pancreatic lesions suggesting that qualitatively assessed hypoenhancement is highly predictive for adenocarcinoma. Additional quantitative analysis of perfusion parameters does not add diagnostic yield. In case of the various hyperenhanced pancreatic lesions in our data set, histologic sampling is essential for further treatment.

Impact of endoscopic ultrasonography on diagnosis of pancreatic cancer

Accumulated evidence has revealed that endoscopic ultrasonography (EUS) has had a great impact on the clinical evaluation of pancreatic cancers. EUS can provide high-resolution images of the pancreas with a quality regarded as far surpassing that achieved on transabdominal ultrasound (US), computed tomography (CT), or magnetic resonance imaging (MRI). EUS is particularly useful for the detection of small pancreatic lesions, while EUS and its related techniques such as contrast-enhanced EUS (CE-EUS), EUS elastography, and EUS-guided fine needle aspiration (EUS-FNA) are also useful in the differential diagnosis of solid or cystic pancreatic lesions and the staging (T-staging, N-staging, and M-staging) of pancreatic cancers. In the diagnosis of pancreatic lesions, CE-EUS and EUS elastography play a complementary role to conventional EUS. When sampling is performed using EUS-FNA, CE-EUS and EUS elastography provide information on the target lesions. Thus, conventional EUS, CE-EUS, EUS elastography, and EUS-FNA are essential in the clinical investigation of pancreatic cancer.

Dynamic contrast-enhanced EUS for quantification of tumor perfusion in colonic cancer: a prospective cohort study

BACKGROUND AND AIMS

Dynamic contrast-enhanced EUS (CE-EUS) for quantification of perfusion in colonic tumors has not previously been reported in the literature. The aim of this study was to investigate correlations between perfusion parameters and vessel density assessed by immunohistochemical staining with antibodies toward CD31 and CD105.

METHODS

We conducted a prospective clinical study of 28 patients with left-sided colonic adenocarcinoma who underwent CE-EUS and left-sided hemicolectomy within 2 weeks. CE-EUS recordings were analyzed in 2 regions of interest: the entire tumor and the most enhanced area. Immunohistochemical staining with CD31 and CD105 was performed on tumor tissue sections. The slides were manually scanned for highly vascularized areas, and counting of vessels was performed in hotspots within the tumor and invasive front. New vasculature was assessed by CD105. Associations between CE-EUS and CD31 and CD105 were investigated using Spearman correlation.

RESULTS

We found significant P values for the correlation between CD31 and rise time (rho = .603 [95% confidence interval (95% CI), .238-.816]; P = .001) in tumor tissue and for the correlation between CD31 and rise time (rho = .50 [95% CI, .201-.695]; P = .008) and fall time (rho = .52 [95% CI, .204-.723]; P = .006) corresponding to the invasive front. We found no correlations between perfusion values evaluated by CE-EUS and CD105.

CONCLUSIONS

Our results show a significant correlation for vessel density evaluated by CD31 and perfusion parameters evaluated by CE-EUS. This may be the first step toward using real-time CE-EUS for monitoring antiangiogenic therapies in colonic cancer. (Clinical trial registration number: NCT02324023.).

Advanced EUS Guided Tissue Acquisition Methods for Pancreatic Cancer

Pancreas cancer is a lethal cancer as the majority patients are diagnosed at an advanced incurable stage. Despite improvements in diagnostic modalities and management strategies, including surgery and chemotherapies, the outcome of pancreas cancer remains poor. Endoscopic ultrasound (EUS) is an important imaging tool for pancreas cancer. For decades, resected pancreas cancer and other cancer specimens have been used to identify tissue biomarkers or genomics for precision therapy; however, only 20% of patients undergo surgery, and thus, this framework is not useful for unresectable pancreas cancer. With advancements in needle technologies, tumor specimens can be obtained at the time of tissue diagnosis. Tumor tissue can be used for development of personalized cancer treatment, such as performing whole exome sequencing and global genomic profiling of pancreas cancer, development of tissue biomarkers, and targeted mutational assays for precise chemotherapy treatment. In this review, we discuss the recent advances in tissue acquisition of pancreas cancer.

EUS and related technologies for the diagnosis and treatment of pancreatic disease: research gaps and opportunities-Summary of a National Institute of Diabetes and Digestive and Kidney Diseases workshop

A workshop was sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases to address the research gaps and opportunities in pancreatic EUS. The event occurred on July 26, 2017 in 4 sessions: (1) benign pancreatic diseases, (2) high-risk pancreatic diseases, (3) diagnostic and therapeutics, and (4) new technologies. The current state of knowledge was reviewed, with identification of numerous gaps in knowledge and research needs. Common themes included the need for large multicenter consortia of various pancreatic diseases to facilitate meaningful research of these entities; to standardize EUS features of different pancreatic disorders, the technique of sampling pancreatic lesions, and the performance of various therapeutic EUS procedures; and to identify high-risk disease early at the cellular level before macroscopic disease develops. The need for specialized tools and accessories to enable the safe and effective performance of therapeutic EUS procedures also was discussed.

Chronic pancreatitis

PURPOSE OF REVIEW

Summarize key clinical advances in chronic pancreatitis reported in 2016.

RECENT FINDINGS

Early diagnosis of chronic pancreatitis remains elusive. Recent studies suggest that endoscopic ultrasound may be less accurate than previously thought and new MRI techniques may be helpful. Genetic predisposition may independently affect the clinical course of chronic pancreatitis and the risk for pancreatic cancer. Cigarette smoking may have a greater negative impact on chronic pancreatitis than previously thought and moderate alcohol consumption may be protective. A multidisciplinary approach is necessary for the treatment of type 3 diabetes and nutritional deficiencies in chronic pancreatitis. Although endoscopic therapy remains a reasonable first-line option in treating chronic pancreatitis and its complications, early surgical intervention may be indicated for pain in select patients.

SUMMARY

Newer endoscopic ultrasound and MRI techniques are being evaluated to help with the early diagnosis of chronic pancreatitis. Both genetic predisposition and cigarette smoking are increasingly recognized as having a major impact in the course of the disease and the risk for pancreatic cancer. Endoscopic therapy is well tolerated and effective for the treatment of chronic pancreatitis and its complications although an early surgical approach for pain may be associated with improved clinical outcomes.

Contrast-enhanced harmonic endoscopic ultrasonography in gallbladder cancer and pancreatic cancer

Endoscopic ultrasonography (EUS) plays a major role in diagnosing gallbladder (GB) cancer and pancreatic cancer (PC). In cases of GB cancer, EUS allows for precise observations of morphology and wall layers. However, proficiency is required for the morphologic diagnosis of GB tumors. Therefore, contrast-enhanced harmonic EUS (CH-EUS) began to be performed to diagnose GB lesions. CH-EUS enables real-time observation of the hemodynamics of GB tumors. The enhanced patterns generated by CH-EUS improve precision in the diagnosis of such tumors.PC appears as a hypoechoic mass on EUS. However, distinguishing between PC and mass-forming pancreatitis or focal autoimmune pancreatitis (AIP) is difficult via conventional EUS. CH-EUS allows for differentiating among these diseases (PC is hypoenhanced and heterogeneously enhanced, pancreatitis is isoenhanced, and a pancreatic neuroendocrine tumor is hyperenhanced). EUS-guided fine needle aspiration (EUS-FNA) also contributes to pathological diagnoses of pancreatic lesions. However, certain PC patients cannot be diagnosed via EUS-FNA. PC is heterogeneously enhanced on CH-EUS, and unenhanced regions have been reported to be areas of fibrosis or necrosis. CH-EUS-guided fine needle aspiration (CH-EUS-FNA) permits puncturing of the enhanced area while avoiding necrotic and fibrotic regions. Moreover, as CH-EUS findings have been quantitatively analyzed, a time-intensity curve (TIC) has become usable for diagnosing solid pancreatic lesions. CH-EUS-related techniques have been developed and increasingly utilized in the pancreaticobiliary area.

Contrast-enhanced endoscopic ultrasound for differential diagnosis of pancreatic cancer: an updated meta-analysis

Aim

We aim to assess the diagnostic value of contrast-enhanced endoscopic ultrasound (CE-EUS) for pancreatic cancer and inflammatory lesions by pooling current evidence.

Materials and Methods

A systematical search of PubMed, Web of Science and the Cochrane Library was performed from inception to January 2016. Two authors independently screened and extracted detailed data from included studies. A random effect model was adopted to estimate the pooled sensitivity, specificity in order to determine the diagnostic ablitity of CE-EUS. Furthermore, we conducted the meta-regression and subgroup analyses to explore possible heterogeneity.

Results

Eighteen eligible studies enrolling 1668 patients were finally included in the study. The pooled sensitivity of CE-EUS for distinguishing pancreatic cancers from solid inflammatory masses was 0.93 (95% CI, 0.91–0.94), and the specificity was 0.88 (95% CI, 0.84–0.90). The area under summary receiver operating characteristic curve yielded 0.97. No publication bias was observed by Deeks’ funnel plot in current meta-analysis.

Conclusions

We provided evidence that CE-EUS is a promising modality for differential diagnosis of pancreatic adenocarcinomas. Further multicenter prospective studies should be carried out to certify its utility.

Imaging modalities for characterising focal pancreatic lesions

BACKGROUND

Increasing numbers of incidental pancreatic lesions are being detected each year. Accurate characterisation of pancreatic lesions into benign, precancerous, and cancer masses is crucial in deciding whether to use treatment or surveillance. Distinguishing benign lesions from precancerous and cancerous lesions can prevent patients from undergoing unnecessary major surgery. Despite the importance of accurately classifying pancreatic lesions, there is no clear algorithm for management of focal pancreatic lesions.

OBJECTIVES

To determine and compare the diagnostic accuracy of various imaging modalities in detecting cancerous and precancerous lesions in people with focal pancreatic lesions.

SEARCH METHODS

We searched the CENTRAL, MEDLINE, Embase, and Science Citation Index until 19 July 2016. We searched the references of included studies to identify further studies. We did not restrict studies based on language or publication status, or whether data were collected prospectively or retrospectively.

SELECTION CRITERIA

We planned to include studies reporting cross-sectional information on the index test (CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), EUS (endoscopic ultrasound), EUS elastography, and EUS-guided biopsy or FNA (fine-needle aspiration)) and reference standard (confirmation of the nature of the lesion was obtained by histopathological examination of the entire lesion by surgical excision, or histopathological examination for confirmation of precancer or cancer by biopsy and clinical follow-up of at least six months in people with negative index tests) in people with pancreatic lesions irrespective of language or publication status or whether the data were collected prospectively or retrospectively.

DATA COLLECTION AND ANALYSIS

Two review authors independently searched the references to identify relevant studies and extracted the data. We planned to use the bivariate analysis to calculate the summary sensitivity and specificity with their 95% confidence intervals and the hierarchical summary receiver operating characteristic (HSROC) to compare the tests and assess heterogeneity, but used simpler models (such as univariate random-effects model and univariate fixed-effect model) for combining studies when appropriate because of the sparse data. We were unable to compare the diagnostic performance of the tests using formal statistical methods because of sparse data.

MAIN RESULTS

We included 54 studies involving a total of 3,196 participants evaluating the diagnostic accuracy of various index tests. In these 54 studies, eight different target conditions were identified with different final diagnoses constituting benign, precancerous, and cancerous lesions. None of the studies was of high methodological quality. None of the comparisons in which single studies were included was of sufficiently high methodological quality to warrant highlighting of the results. For differentiation of cancerous lesions from benign or precancerous lesions, we identified only one study per index test. The second analysis, of studies differentiating cancerous versus benign lesions, provided three tests in which meta-analysis could be performed. The sensitivities and specificities for diagnosing cancer were: EUS-FNA: sensitivity 0.79 (95% confidence interval (CI) 0.07 to 1.00), specificity 1.00 (95% CI 0.91 to 1.00); EUS: sensitivity 0.95 (95% CI 0.84 to 0.99), specificity 0.53 (95% CI 0.31 to 0.74); PET: sensitivity 0.92 (95% CI 0.80 to 0.97), specificity 0.65 (95% CI 0.39 to 0.84). The third analysis, of studies differentiating precancerous or cancerous lesions from benign lesions, only provided one test (EUS-FNA) in which meta-analysis was performed. EUS-FNA had moderate sensitivity for diagnosing precancerous or cancerous lesions (sensitivity 0.73 (95% CI 0.01 to 1.00) and high specificity 0.94 (95% CI 0.15 to 1.00), the extremely wide confidence intervals reflecting the heterogeneity between the studies). The fourth analysis, of studies differentiating cancerous (invasive carcinoma) from precancerous (dysplasia) provided three tests in which meta-analysis was performed. The sensitivities and specificities for diagnosing invasive carcinoma were: CT: sensitivity 0.72 (95% CI 0.50 to 0.87), specificity 0.92 (95% CI 0.81 to 0.97); EUS: sensitivity 0.78 (95% CI 0.44 to 0.94), specificity 0.91 (95% CI 0.61 to 0.98); EUS-FNA: sensitivity 0.66 (95% CI 0.03 to 0.99), specificity 0.92 (95% CI 0.73 to 0.98). The fifth analysis, of studies differentiating cancerous (high-grade dysplasia or invasive carcinoma) versus precancerous (low- or intermediate-grade dysplasia) provided six tests in which meta-analysis was performed. The sensitivities and specificities for diagnosing cancer (high-grade dysplasia or invasive carcinoma) were: CT: sensitivity 0.87 (95% CI 0.00 to 1.00), specificity 0.96 (95% CI 0.00 to 1.00); EUS: sensitivity 0.86 (95% CI 0.74 to 0.92), specificity 0.91 (95% CI 0.83 to 0.96); EUS-FNA: sensitivity 0.47 (95% CI 0.24 to 0.70), specificity 0.91 (95% CI 0.32 to 1.00); EUS-FNA carcinoembryonic antigen 200 ng/mL: sensitivity 0.58 (95% CI 0.28 to 0.83), specificity 0.51 (95% CI 0.19 to 0.81); MRI: sensitivity 0.69 (95% CI 0.44 to 0.86), specificity 0.93 (95% CI 0.43 to 1.00); PET: sensitivity 0.90 (95% CI 0.79 to 0.96), specificity 0.94 (95% CI 0.81 to 0.99). The sixth analysis, of studies differentiating cancerous (invasive carcinoma) from precancerous (low-grade dysplasia) provided no tests in which meta-analysis was performed. The seventh analysis, of studies differentiating precancerous or cancerous (intermediate- or high-grade dysplasia or invasive carcinoma) from precancerous (low-grade dysplasia) provided two tests in which meta-analysis was performed. The sensitivity and specificity for diagnosing cancer were: CT: sensitivity 0.83 (95% CI 0.68 to 0.92), specificity 0.83 (95% CI 0.64 to 0.93) and MRI: sensitivity 0.80 (95% CI 0.58 to 0.92), specificity 0.81 (95% CI 0.53 to 0.95), respectively. The eighth analysis, of studies differentiating precancerous or cancerous (intermediate- or high-grade dysplasia or invasive carcinoma) from precancerous (low-grade dysplasia) or benign lesions provided no test in which meta-analysis was performed.There were no major alterations in the subgroup analysis of cystic pancreatic focal lesions (42 studies; 2086 participants). None of the included studies evaluated EUS elastography or sequential testing.

AUTHORS' CONCLUSIONS

We were unable to arrive at any firm conclusions because of the differences in the way that study authors classified focal pancreatic lesions into cancerous, precancerous, and benign lesions; the inclusion of few studies with wide confidence intervals for each comparison; poor methodological quality in the studies; and heterogeneity in the estimates within comparisons.

A Multidisciplinary Approach to Pancreas Cancer in 2016: A Review

In this article, we review our multidisciplinary approach for patients with pancreatic cancer. Specifically, we review the epidemiology, diagnosis and staging, biliary drainage techniques, selection of patients for surgery, chemotherapy, radiation therapy, and discuss other palliative interventions. The areas of active research investigation and where our knowledge is limited are emphasized.

Maximizing the endosonography: The role of contrast harmonics, elastography and confocal endomicroscopy

New technologies in endoscopic ultrasound (EUS) evaluation have been developed because of the need to improve the EUS and EUS-fine needle aspiration (EUS-FNA) diagnostic rate. This paper reviews the principle, indications, main literature results, limitations and future expectations for each of the methods presented. Contrast-enhanced harmonic EUS uses a low mechanical index and highlights slow-flow vascularization. This technique is useful for differentiating solid and cystic pancreatic lesions and assessing biliary neoplasms, submucosal neoplasms and lymph nodes. It is also useful for the discrimination of pancreatic masses based on their qualitative patterns; however, the quantitative assessment needs to be improved. The detection of small solid lesions is better, and the EUS-FNA guidance needs further research. The differentiation of cystic lesions of the pancreas and the identification of the associated malignancy features represent the main indications. Elastography is used to assess tissue hardness based on the measurement of elasticity. Despite its low negative predictive value, elastography might rule out the diagnosis of malignancy for pancreatic masses. Needle confocal laser endomicroscopy offers useful information about cystic lesions of the pancreas and is still under evaluation for use with solid pancreatic lesions of lymph nodes.

Differentiation of pancreatic carcinoma and mass-forming focal pancreatitis: qualitative and quantitative assessment by dynamic contrast-enhanced MRI combined with diffusion-weighted imaging

Differentiation between pancreatic carcinoma (PC) and mass-forming focal pancreatitis (FP) is invariably difficult. For the differential diagnosis, we qualitatively and quantitatively assessed the value of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in PC and FP in the present study. This study included 32 PC and 18 FP patients with histological confirmation who underwent DCE-MRI and DWI. The time-signal intensity curve (TIC) of PC and FP were classified into 5 types according to the time of reaching the peak, namely, type I, II, III, IV, and V, respectively, and two subtypes, namely, subtype-a (washout type) and subtype-b (plateau type) according to the part of the TIC profile after the peak. Moreover, the mean and relative apparent diffusion coefficient (ADC) value between PC and FP on DWI were compared. The type V TIC was only recognized in PC group (P < 0.01). Type IV b were more frequently observed in PC (P = 0.036), while type- IIa (P < 0.01), type- Ia (P = 0.037) in FP. We also found a significant difference in the mean and relative ADC value between PC and FP. The combined image set of DCE-MRI and DWI yielded an excellent sensitivity, specificity, and diagnostic accuracy (96.9%, 94.4%, and 96.0%). The TIC of DCE-MRI and ADC value of DWI for pancreatic mass were found to provide reliable information in differentiating PC from FP, and the combination of DCE-MRI and DWI can achieve a higher sensitivity, specificity, and diagnostic accuracy.

Dynamic contrast-enhanced endoscopic ultrasound: A quantification method

Dynamic contrast-enhanced ultrasound (DCE-US) has been recently standardized by guidelines and recommendations. The European Federation of Societies for US in Medicine and Biology position paper describes the use for DCE-US. Comparatively, little is known about the use of contrast-enhanced endoscopic US (CE-EUS). This current paper reviews and discusses the clinical use of CE-EUS and DCE-US. The most important clinical use of DCE-US is the prediction of tumor response to new drugs against vascular angioneogenesis.