How to select patients for anti-reflux surgery? The ICARUS guidelines (International Consensus regarding preoperative examinations and clinical characteristics assessment to select adult patients for AntiReflUx Surgery)

Objective: Anti-reflux surgery can be proposed in patients with gastro-esophageal reflux disease, especially when proton pump inhibitor use leads to incomplete symptom improvement. However, to date, international consensus guidelines on the clinical criteria and additional technical examinations used in patient selection for anti-reflux surgery are lacking. We aimed at generating key recommendations in the selection of patients for anti-reflux surgery. Design: We included 35 international experts (gastroenterologists, surgeons and physiologists) in a Delphi process and developed 37 statements that were revised by the Consensus Group, to start the Delphi process. Three voting rounds followed where each statement was presented with the evidence summary. The panel indicated the degree of agreement for the statement. When 80% of the Consensus Group agreed (A+/A) with a statement, this was defined as consensus. All votes were mutually anonymous.Results: Patients with heartburn with a satisfactory response to PPIs, patients with a hiatal hernia (HH), patients with esophagitis LA grade B or higher and patients with Barrett’s esophagus are good candidates for anti-reflux surgery. An endoscopy prior to anti-reflux surgery is mandatory and a barium swallow should be performed in patients with suspicion of a HH or short esophagus. Esophageal manometry is mandatory to rule out major motility disorders. Finally, esophageal pH (+/- impedance) monitoring off PPI is mandatory to select patients for anti-reflux surgery, if endoscopy is negative for unequivocal reflux esophagitis. Conclusion: With the ICARUS guidelines, we generated key recommendations for selection of patients for anti-reflux surgery

Improved detection of colorectal adenomas by high-quality colon cleansing

Background and study aims: Reliable adenoma detection requires "adequate" bowel preparation. The adenoma detection rate (ADR) was assessed in patients with high-quality (stool-free) cleansing versus adequate cleansing. Patients and methods: This study was a post-hoc combined analysis of three randomized trials individually powered for cleansing quality assessment. Treatment-independent ADR was assessed versus colon cleansing quality by central readers using the Harefield Cleansing Scale (HCS) and the Boston Bowel Preparation Scale (BBPS). The number needed to treat (NNT) to find an additional patient with at least one adenoma was calculated for high-quality versus adequate-quality cleansing. Results: A total of 1749 patients were included. ADR increased with high-quality versus adequate-quality cleansing: HCS grade A versus B, 39 % (94/242) versus 27 % (336/1229); NNT = 8.7; P < 0.001. ADR also increased with high-quality versus uniform adequate segmental cleansing scores: HCS grade A versus uniform segmental scores 2, 39 % (94/242) versus 26 % (97/379); NNT = 7.5; P < 0.001. ADR increased with top-quality versus adequate segmental cleansing scores: HCS uniform segmental scores 4 versus 2, 54 % (21/39) versus 26 % (97/379); NNT = 3.6; P < 0.001. ADR increased with BBPS 9 versus 6, 43 % (71/166) versus 26 % (247/950); NNT = 6.0; P < 0.001. Right colon ADR increased with top-quality versus adequate cleansing: HCS 4 versus 2, 20 % (25/122) versus 11 % (121/1117); NNT = 10.4; P < 0.001 and BBPS 3 versus 2, 15 % (42/284) versus 11 % (130/1192); NNT = 25.8; P = 0.033. Conclusions: High-quality colon cleansing improves adenoma detection, and it should be a priority for bowel preparations for colonoscopy

Low interobserver agreement among endoscopists in differentiating dysplastic from non-dysplastic lesions during inflammatory bowel disease colitis surveillance

Objectives. During endoscopic surveillance in patients with longstanding colitis, a variety of lesions can be encountered. Differentiation between dysplastic and non-dysplastic lesions can be challenging. The accuracy of visual endoscopic differentiation and interobserver agreement (IOA) has never been objectified. Material and methods. We assessed the accuracy of expert and nonexpert endoscopists in differentiating (low-grade) dysplastic from non-dysplastic lesions and the IOA among and between them. An online questionnaire was constructed containing 30 cases including a short medical history and an endoscopic image of a lesion found during surveillance employing chromoendoscopy. Results. A total of 17 endoscopists, 8 experts, and 9 nonexperts assessed all 30 cases. The overall sensitivity and specificity for correctly identifying dysplasia were 73.8% (95% confidence interval (CI) 62.1-85.4) and 53.8% (95% CI 42.6-64.7), respectively. Experts showed a sensitivity of 76.0% (95% CI 63.3-88.6) versus 71.8% (95% CI 58.5-85.1, p = 0.434) for nonexperts, the specificity 61.0% (95% CI 49.3-72.7) versus 47.1% (95% CI 34.6-59.5, p = 0.008). The overall IOA in differentiating between dysplastic and non-dysplastic lesions was fair 0.24 (95% CI 0.21-0.27); for experts 0.28 (95% CI 0.21-0.35) and for nonexperts 0.22 (95% CI 0.17-0.28). The overall IOA for differentiating between subtypes was fair 0.21 (95% CI 0.20-0.22); for experts 0.19 (95% CI 0.16-0.22) and nonexpert 0.23 (95% CI 0.20-0.26). Conclusion. In this image-based study, both expert and nonexpert endoscopists cannot reliably differentiate between dysplastic and non-dysplastic lesions. This emphasizes that all lesions encountered during colitis surveillance with a slight suspicion of containing dysplasia should be removed and sent for pathological assessment

Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (BLI)

Objectives: A negative predictive value of more than 90% is proposed by the American Society of Gastrointestinal Endoscopy Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) statement for a new technology in order to leave distal diminutive colorectal polyps in place without resection. To our knowledge, no prior prospective study has yet evaluated the feasibility of the most recently introduced blue light imaging (BLI) system for real-time endoscopic prediction of polyp histology for the specific endpoint of leaving hyperplastic polyps in place. Aims: Prospective assessment of real-time prediction of colorectal polyps by using BLI. Material and methods: In total, 177 consecutive patients undergoing screening or surveillance colonoscopy were included. Colorectal polyps were evaluated in real-time by using high-definition endoscopy and the BLI technology without optical magnification. Before resection, the endoscopist described each polyp according to size, shape and surface characteristics (pit and vascular pattern, colour and depression), and histology was predicted with a level of confidence (high or low). Results: Histology was predicted with high confidence in 92.5% of polyps. Sensitivity of BLI for prediction of adenomatous histology was 92.68%, with a specificity and accuracy of 94.87 and 93.75%, respectively. Following the recommendation of the PIVI statement, positive and negative predictive values were calculated with values of 95 and 92.5%, respectively. Prediction of surveillance based on both US and European guidelines was correctly predicted in 91% of patients. Conclusion: The most recently introduced BLI technology is accurate enough to leave distal colorectal polyps in place without resection. BLI also allowed for assignment of postpolypectomy surveillance intervals. This approach therefore has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps.status: publishe

Gastric cancer incidence and mortality trends 2007-2016 in three European countries

Background Increased awareness of gastric cancer risk, easy access to upper endoscopy, and high definition endoscopes with virtual chromoendoscopy may have led to the increase in early diagnosis of gastric cancer observed in recent years in Europe, which may be associated with improved survival. Currently, no data exist on the impact of early diagnosis on survival at a populational level in Europe. Our aim was to assess gastric cancer incidence, early diagnosis, and survival in northwestern and southern European countries with a low-to-moderate incidence of gastric cancer. Methods Data on 41 138 gastric cancers diagnosed in 2007 2016 were retrieved from national cancer registries of Belgium, the Netherlands, and northern Portugal. Agestandardized incidence and mortality rates were assessed and expressed per 100 000 person-years. Early diagnosis was defined as T1 tumors. Net survival estimates for 2007 2011 vs. 2012 2016 were compared. Results Age-standardized incidence and mortality decreased over time in Belgium, northern Portugal, and the Netherlands (relative incidence decrease 8.6 %, 4.5%, and 46.8%, respectively; relative mortality decrease 22.0%, 30.9%, and 50.0 %, respectively). Early gastric cancer diagnosis increased over time for all countries. Net 1-year survival improved significantly between the two time periods in all countries, and at 5 years in Belgium and Portugal. Conclusions This is the first study comparing trends (2007 2016) in gastric cancer incidence and mortality in some European countries. We found an increasing proportion of T1 gastric cancers and a decrease in age-standardized mortality over time, supporting the use of secondary prevention strategies

Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists

Introduction. Barrett’s oesophagus (BE) is a precursor to oesophageal adenocarcinoma (OAC). Endoscopic surveillance is performed to detect dysplasia arising in BE as it is likely to be amenable to curative treatment. At present, there are no guidelines on who should perform surveillance endoscopy in BE. Machine learning (ML) is a branch of artificial intelligence (AI) that generates simple rules, known as decision trees (DTs). We hypothesised that a DT generated from recognised expert endoscopists could be used to improve dysplasia detection in non-expert endoscopists. To our knowledge, ML has never been applied in this manner. Methods. Video recordings were collected from patients with non-dysplastic (ND-BE) and dysplastic Barrett’s oesophagus (D-BE) undergoing high-definition endoscopy with i-Scan enhancement (PENTAX®). A strict protocol was used to record areas of interest after which a corresponding biopsy was taken to confirm the histological diagnosis. In a blinded manner, videos were shown to 3 experts who were asked to interpret them based on their mucosal and microvasculature patterns and presence of nodularity and ulceration as well as overall suspected diagnosis. Data generated were entered into the WEKA package to construct a DT for dysplasia prediction. Non-expert endoscopists (gastroenterology specialist registrars in training with variable experience and undergraduate medical students with no experience) were asked to score these same videos both before and after web-based training using the DT constructed from the expert opinion. Accuracy, sensitivity, and specificity values were calculated before and after training where p<0.05 was statistically significant. Results. Videos from 40 patients were collected including 12 both before and after acetic acid (ACA) application. Experts’ average accuracy for dysplasia prediction was 88%. When experts’ answers were entered into a DT, the resultant decision model had a 92% accuracy with a mean sensitivity and specificity of 97% and 88%, respectively. Addition of ACA did not improve dysplasia detection. Untrained medical students tended to have a high sensitivity but poor specificity as they “overcalled” normal areas. Gastroenterology trainees did the opposite with overall low sensitivity but high specificity. Detection improved significantly and accuracy rose in both groups after formal web-based training although it did it reach the accuracy generated by experts. For trainees, sensitivity rose significantly from 71% to 83% with minimal loss of specificity. Specificity rose sharply in students from 31% to 49% with no loss of sensitivity. Conclusion. ML is able to define rules learnt from expert opinion. These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. This opens the door to standardised training and assessment of competence for those who perform endoscopy in BE. It may shorten the learning curve and might also be used to compare competence of trainees with recognised experts as part of their accreditation process

Blue-light imaging and linked-color imaging improve visualization of Barrett's neoplasia by nonexpert endoscopists

BACKGROUND AND AIMS: Endoscopic recognition of early Barrett's neoplasia is challenging. Blue-light imaging (BLI) and linked-color imaging (LCI) may assist endoscopists in appreciation of neoplasia. Our aim was to evaluate BLI and LCI for visualization of Barrett's neoplasia in comparison with white-light endoscopy (WLE) alone, when assessed by nonexpert endoscopists. METHODS: In this web-based assessment, corresponding WLE, BLI, and LCI images of 30 neoplastic Barrett's lesions were delineated by 3 expert endoscopists to establish ground truth. These images were then scored and delineated by 76 nonexpert endoscopists from 3 countries and with different levels of expertise, in 4 separate assessment phases with a washout period of 2 weeks. Assessments were as follows: assessment 1, WLE only; assessment 2, WLE + BLI; assessment 3, WLE + LCI; assessment 4, WLE + BLI + LCI. The outcomes were (1) appreciation of macroscopic appearance and ability to delineate lesions (visual analog scale [VAS] scores); (2) preferred technique (ordinal scores); and (3) assessors' delineation performance in terms of overlap with expert ground truth. RESULTS: Median VAS scores for phases 2 to 4 were significantly higher than in phase 1 (P < .001). Assessors preferred BLI and LCI over WLE for appreciation of macroscopic appearance (P < .001) and delineation (P < .001). Linear mixed-effect models showed that delineation performance increased significantly in phase 4. CONCLUSIONS: The use of BLI and LCI has significant additional value for the visualization of Barrett's neoplasia when used by nonexpert endoscopists. Assessors appreciated the addition of BLI and LCI better than the use of WLE alone. Furthermore, this addition led to improved delineation performance, thereby allowing for better acquisition of targeted biopsy samples. (The Netherlands Trial Registry number: NL7541.).status: publishe

The Argos project: The development of a computer‐aided detection system to improve detection of Barrett's neoplasia on white light endoscopy

Background: Computer-aided detection (CAD) systems might assist endoscopists in the recognition of Barrett's neoplasia. Aim: To develop a CAD system using endoscopic images of Barrett's neoplasia. Methods: White light endoscopy (WLE) overview images of 40 neoplastic Barrett's lesions and 20 non-dysplastic Barret's oesophagus (NDBO) patients were prospectively collected. Experts delineated all neoplastic images. The overlap area of at least four delineations was labelled as the ‘sweet spot’. The area with at least one delineation was labelled as the ‘soft spot’. The CAD system was trained on colour and texture features. Positive features were taken from the sweet spot and negative features from NDBO images. Performance was evaluated using leave-one-out cross-validation. Outcome parameters were diagnostic accuracy of the CAD system per image, and localization of the expert soft spot by CAD delineation (localization score) and its indication of preferred biopsy location (red-flag indication score). Results: Accuracy, sensitivity and specificity for detection were 92, 95 and 85%, respectively. The system localized and red-flagged the soft spot in 100 and 90%, respectively. Conclusion: This uniquely trained and validated CAD system detected and localized early Barrett's neoplasia on WLE images with high accuracy. This is an important step towards real-time automated detection of Barrett's neoplasia