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Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.

Background Mantle cell lymphoma (MCL) is an aggressive subtype of B-cell non-Hodgkin lymphoma (NHL), often diagnosed at later stages with secondary gastrointestinal (GI) involvement. Primary GI MCL is rare and is not often discussed in the literature. Aims To increase awareness of a rare condition that is likely to be encountered but can be challenging to diagnose. Methods Case report and review of the literature. Results Case Report A 78-year-old man with multiple untreated vascular risk factors including atrial fibrillation and type 2 diabetes presented with acute onset left hemiplegia, dysarthria, and imaging consistent with a left pontine stroke. As part of his workup he underwent a CT abdomen/pelvis identifying an 11 x 5 cm intraluminal mass in the transverse colon. Previous screening colonoscopies, for family history of colon cancer, were notable for tubular adenomas without high-grade dysplasia at 13, 12, 10, 7, and 2 years prior to admission. The patient had 16 pounds of weight loss without other constitutional symptoms, change in bowel habits or evidence of GI bleeding. Bloodwork was notable for microcytic anemia (Hemoglobin 91 g/L, MCV 75 fL), from a normal baseline one year prior, without other cytopenias. C-reactive protein (44 mg/L) and GGT (164 U/L) were elevated. Other liver enzymes, lactate dehydrogenase, and electrolytes were normal. Colonoscopy revealed numerous polypoid lesions throughout the entire colon and a large non-obstructive mass with submucosal appearance in the transverse colon. Biopsies were taken from the large mass and one of the smaller polypoid lesions. Histology showed a sheet-like infiltrate of small lymphocytes within the lamina propria. Immunohistochemical staining was positive for CD20, BCL2, Cyclin D1, equivocal for CD5, and negative for BCL6 and CD3. Ki67 index approached 30%. A diagnosis of colonic MCL was made. Literature Review Primary MCL of the GI tract is rare, accounting for only 1 to 4% of all GI malignancies. There is a male and Caucasian predominance with a median age of 68 years at diagnosis. Presenting complaints may include abdominal pain, anorexia, and GI bleeding. Typical endoscopic features are small nodular or polypoid tumors, between 2mm and 2 cm in size, along one or more segments of the GI tract referred to as multiple lymphomatous polyposis (MLP). A single colonic mass is infrequently seen, highlighting the importance of endoscopy for diagnosis, as subtle findings may be missed on radiographic evaluation. Biopsies for immunohistochemistry are essential to distinguish MCL from other NHLs, as almost all cases express cyclin D1. Despite aggressive immunochemotherapy, prognosis is often poor due to MCL’s rapid progression and early relapse. Conclusions Primary GI MCL is a rare entity. Awareness is essential as evaluation and management differ from lymphoma at other sites, and other GI malignancies. Funding Agencies None

In 2018, the American Gastroenterological Association's Center for GI Innovation and Technology convened a consensus conference, entitled "Colorectal Cancer Screening and Surveillance: Role of Emerging Technology and Innovation to Improve Outcomes." The conference participants, which included more than 60 experts in colorectal cancer, considered recent improvements in colorectal cancer screening rates and polyp detection, persistent barriers to colonoscopy uptake, and opportunities for performance improvement and innovation. This white paper originates from that conference. It aims to summarize current patient- and physician-centered gaps and challenges in colonoscopy, diagnostic and therapeutic challenges affecting colonoscopy uptake, and the potential use of emerging technologies and quality metrics to improve patient outcomes.

Aims Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI). Methods 401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images. Results EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793. Conclusions To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.

Aims Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel. Methods Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined. Results The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference. Conclusions We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.

Made available in DSpace on 2021-06-26T02:53:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11-16 Bavarian Academic Forum (BayWISS) Background The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. Methods Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. Results The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. Conclusion This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI. Univ Klinikum Augsburg, Med Klin 3, Stenglinstr 2, D-86156 Augsburg, Germany Ostbayer TH Regensburg OTH Regensburg, Regensburg Med Image Comp ReMIC, Regensburg, Germany OTH Regensburg, Regensburg Ctr Hlth Sci & Technol RCHST, Regensburg, Germany Sana Klinikum Lichtenberg, Gastroenterol, Berlin, Germany Asklepios Klin Barmbek, Dept Gastroenterol Hepatol & Intervent Endoscopy, Hamburg, Germany OTH Regensburg, Regensburg Ctr Biomed Engn RCBE, Regensburg, Germany Regensburg Univ, Regensburg, Germany Sao Paulo State Univ, Dept Comp, Sao Paulo, Brazil Catholic Univ Louvain, Clin Univ St Luc, Brussels, Belgium Saku Cent Hosp Adv Care Ctr, Nagano, Japan Klin Hirslanden, GastroZentrum, Zurich, Switzerland Vet Affairs Med Ctr, Dept Gastroenterol & Hepatol, Kansas City, MO USA Univ Kansas, Sch Med, Kansas City, MO USA Univ British Columbia, Vancouver Gen Hosp, Div Gastroenterol, Vancouver, BC, Canada Sao Paulo State Univ, Dept Comp, Sao Paulo, Brazil

Background and Aims Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. Methods A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. Results There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, “EndoNet,” will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. Conclusions Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.

Artificial intelligence (AI) is poised to revolutionize healthcare. Nowhere is this more apparent than in endoscopy as it carries the potential to subvert operator dependence. In this chapter, we discuss existing evidence for AI platforms in endoscopy and explore future applications. This includes lesion detection, lesion characterization, quantifying lesion depth of invasion, inflammatory bowel disease assessment, and the role of AI in endoscopic ultrasound and risk triaging. Moreover, we discuss critical aspects for the effective dissemination of AI in endoscopy including data storage, workflow integration, regulatory, and ethical considerations.

Capsule endoscopy is ideally suited to artificial intelligence based interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software, necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence based clinical applications are likely to proliferate rapidly.

Artificial intelligence (AI) has emerged as a powerful and exciting new technology poised to impact many aspects of health care. In endoscopy, AI is now being used to detect and characterize benign and malignant GI lesions and assess malignant lesion depth of invasion. It will undoubtedly also find use in capsule endoscopy and inflammatory bowel disease. Herein, we provide the general endoscopist with a brief overview of AI and its emerging uses in our field. We also touch on the challenges of incorporating AI into clinical practice, such as workflow integration, data storage, and data privacy.

Background Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

Background Computer vision & deep learning(DL)to assess & help with tissue characterization of disease activity in Ulcerative Colitis(UC)through Mayo Endoscopic Subscore(MES)show good results in central reading for clinical trials.UCEIS(Ulcerative Colitis Endoscopic Index of Severity)being a granular index,may be more reflective of disease activity & more primed for artificial intelligence(AI). We set out to create UC detection & scoring,in a single tool & graphic user interface(GUI),improving accuracy & precision of MES & UCEIS scores & reducing the time elapsed between video collection,quality assurance & final scoring.We apply DL models to detect & filter scorable frames,assess quality of endoscopic recordings & predict MES & UCEIS scores in videos of patients with UC Methods We leveraged>375,000frames from endoscopy cases using Olympus scopes(190&180Series).Experienced endoscopists & 9 labellers tagged~22,000(6%)images showing normal, disease state(MES orUCEIS subscores)& non-scorable frames.We separate total frames in 3 categories:training(60%),testing(20%)&validation(20%).Using a Convolutional Neural Network(CNN)Inception V3,including a biopsy & post-biopsy detector,an out-of-the-body framework & blue light algorithm.Similar architecture for detection with multiple separate units & corresponding dense layers taking CNN to provide continuous scores for 5 separate outputs:MES,aggregate UCEIS & individual components Vascular Pattern,Bleeding & Ulcers. Results Multiple metrics evaluate detection models.Overall performance has an accuracy of~88% & a similar precision & recall for all classes. MAE(distance from ground truth)& mean bias(over/under-prediction tendency)are used to assess the performance of the scoring model.Our model performs well as predicted distributions are relatively close to the labelled,ground truth data & MAE & Bias for all frames are relatively low considering the magnitude of the scoring scale. To leverage all our models,we developed a practical tool that should be used to improve efficiency & accuracy of reading & scoring process for UC at different stages of the clinical journey. Conclusion We propose a DL approach based on labelled images to automate a workflow for improving & accelerating UC disease detection & scoring using MES & UCEIS scores. Our deep learning model shows relevant feature identification for scoring disease activity in UC patients, well aligned with both scoring guidelines,performance of experts & demonstrates strong promise for generalization.Going forward, we aim to continue developing our detection & scoring tool. With our detailed workflow supported by deep learning models, we have a driving function to create a precise & potentially superhuman level AI to score disease activity

Background Central Reading Org.& the pharma industry employ subject matter experts(SMEs)to score videos from sites participating in clinical trials for Ulcerative Colitis(UC).As we are developing Artificial Intelligence(AI)models for scoring purposes,we need to build a new software interface that can incorporate these AI models to aid SMEs,making their determination of the scores for each segment of the video & for the video as a whole.We propose a system that reduces the time for SMEs to review & score videos,improving the accuracy of scoring,with the help of our AI models. Methods We built a web-based interface supported by our AI models which can read,write multiple databases & data stores to read & display videos to be scored by a central reader, as well as the associated metadata required to improve the process.User interface shows a timeline with markers for the segments of the colon,with sections that are blurry,poorly prepped,or unscorable highlighted in different colours.While we could also highlight sections of the video with the precise score assigned to it by the AI,this would bias the central reader’s opinion.We hide the precise score generated by our AI models & instead display 3 colours for low,medium or high disease activity.When a video is loaded to be read by the user,the playback marker is set to the first high disease activity section based on known medical indexes such as the Mayo Endoscopic Subscore(MES) & UCEIS(Ulcerative Colitis Endoscopic Index of Severity),usually consists of a few seconds of video & that video is played back continuously in a loop until the reader selects the appropriate score for that section.When the reader saves the section,the software immediately moves the video cursor to the highest scored section of the video.That way the central reader can review only the relevant portions of the video to confirm the score assigned to each segment.If the central reader’s scores do not align well with the AI scores then the software continues to show more sections of the video to the user,including sections it may have labeled as unscorable,that may be scorable. Results The review of the system by 3 key opinion leaders,user experience was positive.Not only does the system allow the reader’s attention to be more efficiently used,but the interface allows both AI & central reader scores to be saved,allowing for the latter to be used iteratively to re-train & improve the underlying AI model(s).Our tool was also used by a gastroenterologist specialist in order to perform video quality assessment & colon sections scoring. Conclusion We developed an AI tool that can be used to improve the efficiency & accuracy of the central reading process in clinical trials for UC.Further work is ongoing to improve the interface.

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.

BackgroundIn general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the ‘resect and discard’ paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of ‘resect and discard’.Study design and methodsWe developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps.ResultsThe AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%.ConclusionsAn AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the GI tract. They are often noted incidentally at endoscopy or cross sectional imaging. Following the identification of such lesions, further investigation via endoscopic ultrasound, often with fine needle aspiration (EUS-FNA) is used to confirm diagnosis. The natural history and appropriate management of small (< 2 cm) GISTs is not well understood and in 2010 the National Comprehensive Cancer Network (NCCN) guidelines recommended surveillance EUS every 6–12 months. AIMS: To determine if our management strategy for small GISTs was consistent with present guidelines. Secondary outcomes were to identify any barriers to surveillance and determine the natural history of GISTs. METHODS: A retrospective chart review of all EUS procedures at St. Paul’s Hospital, Vancouver, Canada from 01/05-06/17 was completed. Individuals with small (< 2 cm) GISTs were identified. GIST was defined as a hypoechoic lesion arising from the 2nd or 4th layer of the gastrointestinal wall +/- cytological or histologic diagnosis. Data collected included patient demographics, clinical presentation, initial and subsequent EUS findings, interval between EUS, reasons for not undergoing surveillance EUS, and any pathology. Candidates were then collated based on the presence of GISTs and whether appropriate follow-up occurred based on the National Comprehensive Cancer Network (NCCN) guidelines. This study was approved by the IRB at St. Paul’s Hospital. RESULTS: GISTs were identified by EUS in 143 patients, 69 (48%) had lesions

BACKGROUND: Endoscopic ultrasound (EUS) guided Fine Needle Aspiration (FNA) is an effective modality for tissue acquisition in the diagnosis of GI and non-GI pathology. The optimal method of FNA is not known. The main focus is on maximisation of each FNA pass. There is little emphasis on the number of times or actuations the needle is moved within the target lesion during each FNA pass. AIMS: The aim of this study was to determine the optimal number of actuations for obtaining high diagnostic yield in each FNA pass. METHODS: This is a retrospective study carried out at Vancouver General Hospital. EUS guided FNA of solid lesions in the GI tract were included. FNA was performed with a 22G FNA needle. Three different passes were performed on each mass lesion. A standardised technique was adopted for all passes which involved no stylet, fanning, and wet suction at 20cc. There were 10 actuations performed for the 1(st) pass, 20 actuations for the 2(nd) pass, and 30 actuations for the 3(rd) pass. The cellularity and diagnosis for each pass were determined by GI pathologists blinded to patient inclusion in the study. RESULTS: 32 patients were included in this study with 10 males and 22 females. The average age was 68 years with a median age of 67.5 years. Pancreatic masses were the most common tissue type with pancreatic ductal adenocarcinoma being the most common diagnosis. The number of actuations did not affect the ability to obtain a diagnosis. Tissue obtained from all three pass types were considered adequate for evaluation by pathologists. Passes with 10, 20 or 30 actuations provided equal yield in pathologic diagnosis. The 1(st) pass was diagnostic in all 32 cases. CONCLUSIONS: Our data suggest that lower number of actuations can reliably be used during tissue acquisition for EUS guided FNA without affecting the diagnostic yield. Fewer actuations may lower the risk of procedural complications including bleeding and seeding. FUNDING AGENCIES: None

BACKGROUND: Pancreatic metastases (PM) from non-pancreatic primaries are rare, but should be considered in patients with a prior history of malignancy. PM’s represent less than 2% of all pancreatic neoplasms. Studies have not adequately evaluated the ideal oncologic and combined surgical approach for pancreatic metastasis. AIMS: The aim of this study was to investigate the clinico-pathological presentation of patients with secondary tumors of the pancreas and surgical management. METHODS: We retrospectively identified patients from a high volume hepato-pancreato-billiary database in Canada dating from January 1989- September 2015. Medical records were retrospectively analyzed for clinical presentation, pathologic details, time period between the diagnosis of the primary tumor to metastasis or disease free interval (DFI), tumor size, focality and surgical resectability. RESULTS: We identified 103 patients (median age 53.5) with disease metastatic to the pancreas for which cytologic material was available via endoscopic ultrasound (EUS) fine needle aspiration (n=49, 47.6%), EUS core biopsy (n=14, 13.6%) or upfront definitive surgical resection (n=38 36.7%). Renal carcinoma was the most common tumor to metastasize to the pancreas (52%), followed by lung carcinoma (10%), colonic adenocarcinoma (10%), melanoma (6%) and breast (6%) followed by 10 other primary tumors. The median DFI was 6.8 years (inter-quartile range 1.23–12.78) between diagnoses of primary malignancy to metastasis. There was a significant difference in DFI for RCC (median 9.7 years, p

Colorectal cancer (CRC) is the second leading cause of cancer death, and is a significant cause of morbidity and mortality. This is a growing topic discussed on public media networks due to the worldwide rise in CRC incidence among people under 50 years of age and recent American Cancer Society recommendations for earlier CRC screening. Colonoscopy remains the most effective method of detection and removal of neoplastic polyps.

Gastrointestinal (GI) cancer is a leading cause worldwide of morbidity and mortality. In 2018, GI cancer accounted for 27% of all new cancer diagnoses. The incidence rate of colorectal cancer is rising in many countries, with a recent dramatic increase for people under the age of 50 years.