Multitarget Stool DNA Screening in Clinical Practice: High Positive Predictive Value for Colorectal Neoplasia Regardless of Exposure to Previous Colonoscopy
OBJECTIVES: Multitarget stool DNA (MT-sDNA) testing has grown as a noninvasive screening modality for colorectal cancer (CRC), but real-world clinical data are limited in the post-FDA approval setting. The effect of previous colonoscopy on MT-sDNA performance is not known. We aimed to evaluate findings of colorectal neoplasia (CRN) at diagnostic colonoscopy in patients with positive MT-sDNA testing, stratified by patient exposure to previous colonoscopy. METHODS: We identified consecutive patients completing MT-sDNA testing over a 39-month period and reviewed the records of those with positive tests for neoplastic findings at diagnostic colonoscopy. MT-sDNA test positivity rate, adherence to diagnostic colonoscopy, and the positive predictive value (PPV) of MT-sDNA for any CRN and neoplastic subtypes were calculated. RESULTS: Of 16,469 MT-sDNA tests completed, testing returned positive in 2,326 (14.1%) patients. After exclusion of patients at increased risk for CRC, 1,801 patients remained, 1,558 (87%) of whom underwent diagnostic colonoscopy; 918 of 1,558 (59%) of these patients had undergone previous colonoscopy, whereas 640 (41%) had not. Any CRN was found in 1,046 of 1,558 patients (PPV = 67%). More neoplastic lesions were found in patients without previous colonoscopy (73%); however, the rates remained high among those who had undergone previous colonoscopy (63%, P < 0.0001). The large majority (79%) of patients had right-sided neoplasia. DISCUSSION: MT-sDNA has a high PPV for any CRN regardless of exposure to previous colonoscopy. Right-sided CRN was found at colonoscopy in most patients with positive MT-sDNA testing, representing a potential advantage over other currently available screening modalities for CRC.
Early detection and imaging strategies to reveal and target developing pancreatic cancer
AbstractDistal biliary strictures (DBS) are common and may be caused by both malignant and benign pathologies. While endoscopic procedures play a major role in their management, a comprehensive review of the subject is still lacking. Our consensus statements were formulated by a group of expert Asian pancreatico‐biliary interventional endoscopists, following a proposal from the Digestive Endoscopy Society of Taiwan, the Thai Association for Gastrointestinal Endoscopy, and the Tokyo Conference of Asian Pancreato‐biliary Interventional Endoscopy. Based on a literature review utilizing Medline, Cochrane library, and Embase databases, a total of 19 consensus statements on DBS were made on diagnosis, endoscopic drainage, benign biliary stricture, malignant biliary stricture, and management of recurrent biliary obstruction and other complications. Our consensus statements provide comprehensive guidance for the endoscopic management of DBS.
New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection
INTRODUCTION: Mucosal healing is a widely accepted target for ulcerative colitis (UC) treatment. Linked color imaging (LCI) is a novel endoscopic enhancement system that utilizes Light Emitting Diode (LED) sources to spectrally enhance subtle erythema and mucosal surface patterns which may improve endoscopic classification of IBD associated inflammation. We aimed to assess the accuracy of LCI in classifying UC-associated mucosal healing compared to a validated histological reference standard (Geboes score). METHODS: 22 patients with ulcerative colitis were prospectively enrolled and evaluated by LCI colonoscopy using the Fujifilm’s digital video Processor VP-7000 and LED light source BL-7000 (Fujifilm, Tokyo Japan). Interobserver agreement was reported as kappa values. Geboes score was reported as score range of 0 to 5.4. A Geboes score of ≥3.1 (presence of surface intraepithelial neutrophils or < 5% crypts) was used to define histologic activity and a score of < 2B (no increase in neutrophils in the lamina propria) was used to define histologic remission. Geboes score has been previously validated to predict clinical recurrence. We calculated the overall accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the LCI classification identifying mucosal healing against the Geboes score using the Fisher’s exact test. RESULTS: Total of 62 endoscopic images were obtained for different parts of the colon, 43 of which were matched to a corresponding biopsy. Endoscopic images were scored by 2 endoscopists into LCI-A (no redness), LCI-B (redness with visible vessels) and LCI-C (redness without visible vessels). A final LCI score was given for images with discrepant scores based on a consensus vote from both endoscopists. The interobserver agreement between the two endoscopists was 81%, kappa of 0.71 (95% confidence interval [CI] 0.57- 0.86). Among regions scored ≥3.1 on Geboes score, 9.5%, 28.6% and 61.9 % were classified as LCI-A, LCI-B and LCI-C, respectively ( P < 0.0001). Among images scored < 2B on Geboes score, 81.8%, 13.6% and 4.6% were classified as LCI-A, LCI-B and LCI-C, respectively ( P < 0.0001). Analyzing data against histology with LCI-A representing mucosal healing and LCI-B and C representing active mucosal disease, the overall accuracy becomes 86%, with NPV of 82.6% and PPV of 90% for identifying UC patients with histological and endoscopic evidence of mucosal healing. CONCLUSION: LCI technology has a potential role in assessing mucosal healing in UC.
Development of a stratification tool to identify pancreatic intraductal papillary mucinous neoplasms at lowest risk of progression
SummaryBackgroundBecause most pancreatic intraductal papillary mucinous neoplasms (IPMNs) will never become malignant, currently advocated long‐term surveillance is low‐yield for most individuals.AimTo develop a score chart identifying IPMNs at lowest risk of developing worrisome features or high‐risk stigmata.MethodsWe combined prospectively maintained pancreatic cyst surveillance databases of three academic institutions. Patients were included if they had a presumed side‐branch IPMN, without worrisome features or high‐risk stigmata at baseline (as defined by the 2012 international Fukuoka guidelines), and were followed ≥ 12 months. The endpoint was development of one or more worrisome features or high‐risk stigmata during follow‐up. We created a multivariable prediction model using Cox‐proportional logistic regression analysis and performed an internal‐external validation.Results875 patients were included. After a mean follow‐up of 50 months (range 12‐157), 116 (13%) patients developed worrisome features or high‐risk stigmata. The final model included cyst size (HR 1.12, 95% CI 1.09‐1.15), cyst multifocality (HR 1.49, 95% CI 1.01‐2.18), ever having smoked (HR 1.40, 95% CI 0.95‐2.04), history of acute pancreatitis (HR 2.07, 95% CI 1.21‐3.55), and history of extrapancreatic malignancy (HR 1.34, 95% CI 0.91‐1.97). After validation, the model had good discriminative ability (C‐statistic 0.72 in the Mayo cohort, 0.71 in the Columbia cohort, 0.64 in the Erasmus cohort).ConclusionIn presumed side branch IPMNs without worrisome features or high‐risk stigmata at baseline, the Dutch‐American Risk stratification Tool (DART‐1) successfully identifies pancreatic lesions at low risk of developing worrisome features or high‐risk stigmata.
Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging
Objective This study aimed to evaluate a deep learning protocol to identify neoplasia in intraductal papillary mucinous neoplasia (IPMN) in comparison to current radiographic criteria. Methods A computer-aided framework was designed using convolutional neural networks to classify IPMN. The protocol was applied to magnetic resonance images of the pancreas. Features of IPMN were classified according to American Gastroenterology Association guidelines, Fukuoka guidelines, and the new deep learning protocol. Sensitivity and specificity were calculated using surgically resected cystic lesions or healthy controls. Results Of 139 cases, 58 (42%) were male; mean (standard deviation) age was 65.3 (11.9) years. Twenty-two percent had normal pancreas; 34%, low-grade dysplasia; 14%, high-grade dysplasia; and 29%, adenocarcinoma. The deep learning protocol sensitivity and specificity to detect dysplasia were 92% and 52%, respectively. Sensitivity and specificity to identify high-grade dysplasia or cancer were 75% and 78%, respectively. Diagnostic performance was similar to radiologic criteria. Areas under the receiver operating curves (95% confidence interval) were 0.76 (0.70–0.84) for American Gastroenterology Association, 0.77 (0.70–0.85) for Fukuoka, and 0.78 (0.71–0.85) for the deep learning protocol ( P = 0.90). Conclusions The deep learning protocol showed accuracy comparable to current radiographic criteria. Computer-aided frameworks could be implemented as aids for radiologists to identify high-risk IPMN.
Enhanced family history screening is crucial to individualized cancer surveillance.
e13161 Background: Capturing family health history is a simple and cost-effective way to identify individuals at increased risk for hereditary cancers. Several barriers exist to family history collection in the clinical setting. Self-reported histories can be limited, inaccurate or static. Physicians are often overwhelmed with data and “alert fatigue.” These complex factors make it difficult to quantify hereditary risks. Patients with increased genetic predisposition may be missed and may not receive individualized cancer surveillance measures. Methods: A practice improvement project was implemented at Mayo Clinic Florida to enhance family history screening. A questionnaire and simple scoring algorithm were developed to prompt referral for genetic counseling. This tool was pilot tested in a multi-specialty gastroenterology clinic. The questionnaire was administered in the waiting room (tablet interface). Patients with significant risk factors identified were contacted and offered genetic counseling. Results: Currently, 291 patients have completed the questionnaire. Thirty of the 60 patients identified to have significant familial risk factors met with a genetic counselor. Of the twenty who completed genetic testing, 9 patients received one or more variants of unknown significance. Two pathogenic variants were identified. One patient was molecularly confirmed to have multiple endocrine neoplasia type 2, which can allow for cascade screening in her unaffected sibling. A pathogenic ATM variant was found in a patient with a recent personal history of pancreatic cancer. The result prompted consideration of platinum based chemotherapy and/or PARP inhibitors in her cancer management. She has had a visible response thus far with the treatment. Her sister implemented a new surveillance regimen after testing positive for the familial variant. Conclusions: Enhanced family history screening is a low cost, effective method for the identification of patients who may benefit from hereditary cancer screening and genetic testing. Increased attention to family history is a vital component of precision medicine in any care setting.
Volumetric laser endomicroscopy and its application to Barrett's esophagus: results from a 1,000 patient registry
SUMMARY Volumetric laser endomicroscopy (VLE) uses optical coherence tomography (OCT) for real-time, microscopic cross-sectional imaging. A US-based multi-center registry was constructed to prospectively collect data on patients undergoing upper endoscopy during which a VLE scan was performed. The objective of this registry was to determine usage patterns of VLE in clinical practice and to estimate quantitative and qualitative performance metrics as they are applied to Barrett's esophagus (BE) management. All procedures utilized the NvisionVLE Imaging System (NinePoint Medical, Bedford, MA) which was used by investigators to identify the tissue types present, along with focal areas of concern. Following the VLE procedure, investigators were asked to answer six key questions regarding how VLE impacted each case. Statistical analyses including neoplasia diagnostic yield improvement using VLE was performed. One thousand patients were enrolled across 18 US trial sites from August 2014 through April 2016. In patients with previously diagnosed or suspected BE (894/1000), investigators used VLE and identified areas of concern not seen on white light endoscopy (WLE) in 59% of the procedures. VLE imaging also guided tissue acquisition and treatment in 71% and 54% of procedures, respectively. VLE as an adjunct modality improved the neoplasia diagnostic yield by 55% beyond the standard of care practice. In patients with no prior history of therapy, and without visual findings from other technologies, VLE-guided tissue acquisition increased neoplasia detection over random biopsies by 700%. Registry investigators reported that VLE improved the BE management process when used as an adjunct tissue acquisition and treatment guidance tool. The ability of VLE to image large segments of the esophagus with microscopic cross-sectional detail may provide additional benefits including higher yield biopsies and more efficient tissue acquisition. Clinicaltrials.gov NCT02215291