Learning curves and the influence of procedural volume for the treatment of dysplastic Barrett's esophagus

BACKGROUND AND AIMS: Endoscopic resections (ER) and radiofrequency ablation (RFA) are the established treatments for Barrett's-associated dysplasia and early esophageal neoplasia. The UK RFA Registry collects patient outcomes from 24 centers in the United Kingdom and Ireland treating patients. Learning curves for treatment of Barrett's dysplasia and the impact of center caseload on patient outcomes is still unknown. METHODS: We examined outcomes of 678 patients treated with RFA in the UK Registry using risk-adjusted CUSUM plots to identify change points in complete resolution of intestinal metaplasia (CR-IM) and complete resolution of dysplasia (CR-D) outcomes. We compared outcomes between those treated at high- (>100 enrolled patients), medium- (51-100) and low- (<50) volume centers. RESULTS: There was no association between center volume and CR-IM and CR-D rates, but there were lower recurrence rates in high-volume versus low-volume centers (Log Rank p=0.001).There was a significant change-point for outcomes at 12 cases for CR-D (reduction from 24.5% to 10.4%; P<0.001) and at 18 cases for CR-IM (30.7% to 18.6%; P<0.001) from RA-CUSUM curve analysis. CONCLUSION: Our data suggest that 18 supervised cases of endoscopic ablation may be required before competency in endoscopic treatment of Barrett's dysplasia can be achieved. The difference in outcomes between a high-volume and low-volume center does not support further centralization of services to only high-volume centers

Bariatric and Metabolic Endoscopy: A New Paradigm.

The prevalence of obesity, type 2 diabetes mellitus, and metabolic syndromes is increasing globally. Minimally invasive metabobariatric (MB) endoscopic therapies are adjunct treatments that can potentially bridge the gap between surgical interventions and medical therapy. A growing number of MB techniques are becoming available, allowing for more personalized and patient-targeted treatment options for specific disease states. MB techniques are less invasive than surgery and can precisely target different parts of the gastrointestinal tract that may be responsible for the pathophysiology of obesity and metabolic syndromes such as type 2 diabetes mellitus. These alternatives should be selected on an individualized patient basis to balance the expected clinical outcomes and desired anatomical targets with the level of invasiveness and degree of acceptable risk. Each MB intervention presents great flexibility allowing for a tailored intervention and different levels of patient engagement. Patient awareness and motivation are essential to avoid therapy withdrawal and failure. Differences between MB procedures in terms of weight loss and metabolic benefit will be discussed in this review, along with the insights on clinical decision-making processes to evaluate the potential of further evolution and growth of bariatric and metabolic endoscopy

A cost-effectiveness analysis of endoscopic eradication therapy for management of dysplasia arising in patients with Barrett's oesophagus in the United Kingdom

BACKGROUND AND AIMS: Endoscopic eradication therapy (EET) is the first line approach for treating Barrett's Esophagus (BE) related neoplasia globally. The British Society of Gastroenterology (BSG) recommend EET with combined endoscopic resection (ER) for visible dysplasia followed by endoscopic ablation in patients with both low and high grade dysplasia (LGD and HGD). The aim of this study is to perform a cost-effectiveness analysis for EET for treatment of all grades of dysplasia in BE patients. METHODS: A Markov cohort model with a lifetime time horizon was used to undertake a cost effectiveness analysis. A hypothetical cohort of United Kingdom (UK) patients diagnosed with BE entered the model. Patients in the treatment arm with LGD and HGD received EET and patients with non-dysplastic BE (NDBE) received endoscopic surveillance only. In the comparator arm, patients with LGD, HGD and NDBE received endoscopic surveillance only. A UK National Health Service (NHS) perspective was adopted and the incremental cost effectiveness ratio (ICER) was calculated. Sensitivity analysis was conducted on key input parameters. RESULTS: EET for patients with LGD and HGD arising in BE is cost-effective compared to endoscopic surveillance alone (lifetime ICER £3,006 per QALY gained). The results show that as the time horizon increases, the treatment becomes more cost-effective. The five year financial impact to the UK NHS of introducing EET is £7.1m. CONCLUSIONS: EET for patients with low and high grade BE dysplasia, following updated guidelines from the BSG has been shown to be cost-effective for patients with BE in the UK

Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia

OBJECTIVES: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. METHODS: An AI algorithm was evaluated on 4 video test datasets containing 173 polyps (35,114 polyp positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by 8 endoscopists (4 independent, 4 trainees, according to Joint Advisory Group on GI endoscopy (JAG) standards in United Kingdom). RESULTS: In the first 2 video datasets, the algorithm achieved per-polyp sensitivities of 100% and 98.9%. Per-frame sensitivities were 84.1% and 85.2% . In the subtle dataset, the algorithm detected a significantly higher number of polyps (P<0.0001), compared to JAG-independent and trainee endoscopists, achieving per-polyp sensitivities of 79.5%, 37.2% and 11.5% respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. CONCLUSIONS: The AI based algorithm achieved high per-polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer

Upregulation of mucin glycoprotein MUC1 in the progression to esophageal adenocarcinoma and therapeutic potential with a targeted photoactive antibody-drug conjugate

BACKGROUND: Mucin glycoprotein 1 (MUC1) is a glycosylated transmembrane protein on epithelial cells. We investigate MUC1 as a therapeutic target in Barrett's epithelium (BE) and esophageal adenocarcinoma (EA) and provide proof of concept for a light based therapy targeting MUC1. RESULTS: MUC1 was present in 21% and 30% of significantly enriched pathways comparing BE and EA to squamous epithelium respectively. MUC1 gene expression was x2.3 and x2.2 higher in BE (p=<0.001) and EA (p=0.03). MUC1 immunohistochemical expression increased during progression to EA and followed tumor invasion. HuHMFG1 based photosensitive antibody drug conjugates (ADC) showed cell internalization, MUC1 selective and light-dependent cytotoxicity (p=0.0006) and superior toxicity over photosensitizer alone (p=0.0022). METHODS: Gene set enrichment analysis (GSEA) evaluated pathways during BE and EA development and quantified MUC1 gene expression. Immunohistochemistry and flow cytometry evaluated the anti-MUC1 antibody HuHMFG1 in esophageal cells of varying pathological grade. Confocal microscopy examined HuHMFG1 internalization and HuHMFG1 ADCs were created to deliver a MUC1 targeted phototoxic payload. CONCLUSIONS: MUC1 is a promising target in EA. Molecular and light based targeting of MUC1 with a photosensitive ADC is effective in vitro and after development may enable treatment of locoregional tumors endoscopically

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

Systematic assessment with I-SCAN magnification endoscopy and acetic acid improves dysplasia detection in patients with Barrett's esophagus

BACKGROUND AND STUDY AIMS: Enhanced endoscopic imaging with chromoendoscopy may improve dysplasia recognition in patients undergoing assessment of Barrett's esophagus (BE). This may reduce the need for random biopsies to detect more dysplasia. The aim of this study was to assess the effect of magnification endoscopy with I-SCAN (Pentax, Tokyo, Japan) and acetic acid (ACA) on dysplasia detection in BE using a novel mucosal and vascular classification system. METHODS: BE segments and suspicious lesions were recorded with high definition white-light and magnification endoscopy enhanced using all I-SCAN modes in combination. We created a novel mucosal and vascular classification system based on similar previously validated classifications for narrow-band imaging (NBI). A total of 27 videos were rated before and after ACA application. Following validation, a further 20 patients had their full endoscopies recorded and analyzed to model use of the system to detect dysplasia in a routine clinical scenario. RESULTS: The accuracy of the I-SCAN classification system for BE dysplasia improved with I-SCAN magnification from 69 % to 79 % post-ACA (P = 0.01). In the routine clinical scenario model in 20 new patients, accuracy of dysplasia detection increased from 76 % using a "pull-through" alone to 83 % when ACA and magnification endoscopy were combined (P = 0.047). Overall interobserver agreement between experts for dysplasia detection was substantial (0.69). CONCLUSIONS: A new I-SCAN classification system for BE was validated against similar systems for NBI with similar outcomes. When used in combination with magnification and ACA, the classification detected BE dysplasia in clinical practice with good accuracy.Trials registered at ISRCTN (58235785)

A clinically interpretable convolutional neural network for the real time prediction of early squamous cell cancer of the esophagus; comparing diagnostic performance with a panel of expert European and Asian endoscopists

BACKGROUND AND AIMS: Intrapapillary capillary loops (IPCLs) are microvascular structures that correlate with invasion depth of early squamous cell neoplasia (ESCN) and allow accurate prediction of histology. Artificial intelligence may improve human recognition of IPCL patterns and prediction of histology to allow prompt access to endoscopic therapy of ESCN where appropriate METHODS: One hundred fifteen patients were recruited at 2 academic Taiwanese hospitals. ME-NBI videos of squamous mucosa were labeled as dysplastic or normal according to their histology and IPCL patterns classified by consensus of 3 experienced clinicians. A convolutional neural network (CNN) was trained to classify IPCLs, using 67742 high quality ME-NBI by 5-fold cross validation. Performance measures were calculated to give an average F1 score, accuracy, sensitivity, and specificity. A panel of 5 Asian and 4 European experts predicted the histology of a random selection of 158 images using the JES IPCL classification; accuracy, sensitivity, specificity, positive and negative predictive values were calculated. RESULTS: Expert European Union (EU) and Asian endoscopists attained F1 scores (a measure of binary classification accuracy) of 97.0% and 98%, respectively. Sensitivity and accuracy of the EU and Asian clinicians were 97%, 98% and 96.9%, 97.1% respectively. The CNN average F1 score was 94%, sensitivity 93.7% and accuracy 91.7%. Our CNN operates at video rate and generates class activation maps that can be used to visually validate CNN predictions. CONCLUSIONS: We report a clinically interpretable CNN developed to predict histology based on IPCL patterns, in real-time, using the largest reported dataset of images for this purpose. Our CNN achieved diagnostic performance comparable to an expert panel of endoscopists

Improvement over time in outcomes for patients undergoing endoscopic therapy for Barrett's oesophagus-related neoplasia: 6-year experience from the first 500 patients treated in the UK patient registry.

BACKGROUND: Barrett's oesophagus (BE) is a pre-malignant condition leading to oesophageal adenocarcinoma (OAC). Treatment of neoplasia at an early stage is desirable. Combined endoscopic mucosal resection (EMR) followed by radiofrequency ablation (RFA) is an alternative to surgery for patients with BE-related neoplasia. METHODS: We examined prospective data from the UK registry of patients undergoing RFA/EMR for BE-related neoplasia from 2008 to 2013. Before RFA, visible lesions were removed by EMR. Thereafter, patients had RFA 3-monthly until all BE was ablated or cancer developed (endpoints). End of treatment biopsies were recommended at around 12 months from first RFA treatment or when endpoints were reached. Outcomes for clearance of dysplasia (CR-D) and BE (CR-IM) at end of treatment were assessed over two time periods (2008-2010 and 2011-2013). Durability of successful treatment and progression to OAC were also evaluated. RESULTS: 508 patients have completed treatment. CR-D and CR-IM improved significantly between the former and later time periods, from 77% and 56% to 92% and 83%, respectively (p<0.0001). EMR for visible lesions prior to RFA increased from 48% to 60% (p=0.013). Rescue EMR after RFA decreased from 13% to 2% (p<0.0001). Progression to OAC at 12 months is not significantly different (3.6% vs 2.1%, p=0.51). CONCLUSIONS: Clinical outcomes for BE neoplasia have improved significantly over the past 6 years with improved lesion recognition and aggressive resection of visible lesions before RFA. Despite advances in technique, the rate of cancer progression remains 2-4% at 1 year in these high-risk patients. TRIAL REGISTRATION NUMBER: ISRCTN93069556

Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study

BACKGROUND: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth – an important factor in the success of curative endoscopic therapy. IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME-NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions. METHODS: A total of 7046 sequential high-definition ME-NBI images from 17 patients (10 ESCN, 7 normal) were used to train a CNN. IPCL patterns were classified by three expert endoscopists according to the Japanese Endoscopic Society classification. Normal IPCLs were defined as type A, abnormal as B1–3. Matched histology was obtained for all imaged areas. RESULTS: This CNN differentiates abnormal from normal IPCL patterns with 93.7% accuracy (86.2% to 98.3%) and sensitivity and specificity for classifying abnormal IPCL patterns of 89.3% (78.1% to 100%) and 98% (92% to 99.7%), respectively. Our CNN operates in real time with diagnostic prediction times between 26.17 ms and 37.48 ms. CONCLUSION: Our novel and proof-of-concept application of computer-aided endoscopic diagnosis shows that a CNN can accurately classify IPCL patterns as normal or abnormal. This system could be used as an in vivo, real-time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN