Endoscopic Assessment and Treatment of Barrett’s Oesophagus

Oesophageal cancer worldwide is the eighth commonest cancer and carries a poor prognosis. Barrett’s oesophagus is the only known risk factor for oesophageal adenocarcinoma. Cancer progresses along a metaplasia-dysplasia pathway. Dysplastic changes may be seen on endoscopic assessment. This thesis presents evidence that i-Scan virtual chromoendoscopy together with acetic acid chromoendoscopy can improve dysplasia detection using a simple classification system. Superficial lesions, without deeper invasion (low and high grade dysplasia, early cancers) have a low risk of distant metastasis. Endoscopic resection and ablation techniques have been demonstrated to have an excellent efficacy and safety profile. The current standard of care for early Barrett’s neoplasia is endoscopic management rather than surgical intervention. Surgery for oesophageal cancer is centred in specialist units due to improved outcomes in high volume centres. The UK radiofrequency ablation registry collects outcomes for patients undergoing endoscopic therapy for Barrett’s neoplasia. This thesis demonstrates that there is no difference in dysplasia or intestinal metaplasia resolution rates or dysplasia recurrence between low and high volume centres. Learning curve analysis suggests that there is a change point at 18 cases, when the observed successful treatment rate of the centre becomes better than the expected rate. Centres should complete 20 cases before competency can be achieved. Treatment of Barrett’s neoplasia involves endoscopic resection of visible lesions. Due to the high risk of metachronous lesions, the remaining Barrett’s epithelium undergoes field ablation, commonly with radiofrequency ablation. Following successful treatment the risk of dysplasia recurrence is 6%. The risk increases with increasing length of the initial Barrett segment and with increasing age. The risk of untreated islands of Barrett’s IM is unknown but this thesis demonstrates that it does not seem to confer an increased risk of recurrence and may not require further ablation if unresponsive to treatment

Endoscopic eradication therapy for Barrett’s esophagus–related neoplasia: a final 10-year report from the UK National HALO Radiofrequency Ablation Registry

Background and Aims: Long-term durability data for effectiveness of radiofrequency ablation (RFA) to prevent esophageal adenocarcinoma in patients with dysplastic Barrett’s esophagus (BE) are lacking. Methods: We prospectively collected data from 2535 patients with BE (mean length, 5.2 cm; range, 1-20) and neoplasia (20% low-grade dysplasia, 54% high-grade dysplasia, 26% intramucosal carcinoma) who underwent RFA therapy across 28 UK hospitals. We assessed rates of invasive cancer and performed detailed analyses of 1175 patients to assess clearance rates of dysplasia (CR-D) and intestinal metaplasia (CR-IM) within 2 years of starting RFA therapy. We assessed relapses and rates of return to CR-D (CR-D2) and CR-IM (CR-IM2) after further therapy. CR-D and CR-IM were confirmed by an absence of dysplasia and intestinal metaplasia on biopsy samples taken at 2 consecutive endoscopies. Results: Ten years after starting treatment, the Kaplan-Meier (KM) cancer rate was 4.1% with a crude incidence rate of .52 per 100 patient-years. CR-D and CR-IM after 2 years of therapy were 88% and 62.6%, respectively. KM relapse rates were 5.9% from CR-D and 18.7% from CR-IM at 8 years, with most occurring in the first 2 years. Both were successfully retreated with rates of CR-D2 of 63.4% and CR-IM2 of 70.0% 2 years after retreatment. EMR before RFA increased the likelihood of rescue EMR from 17.2% to 41.7% but did not affect the rate of CR-D, whereas rescue EMR after RFA commenced reduced CR-D from 91.4% to 79.7% (χ2 P < .001). Conclusions: RFA treatment is effective and durable to prevent esophageal adenocarcinoma. Most treatment relapses occur early and can be successfully retreated

A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks

BACKGROUND AND AIMS: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS: The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION: Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance

Carprofen elicits pleiotropic mechanisms of bactericidal action with the potential to reverse antimicrobial drug resistance in tuberculosis

Background The rise of antimicrobial drug resistance in Mycobacterium tuberculosis coupled with the shortage of new antibiotics has elevated TB to a major global health priority. Repurposing drugs developed or used for other conditions has gained special attention in the current scenario of accelerated drug development for several global infectious diseases. In a similar effort, previous studies revealed that carprofen, a non-steroidal anti-inflammatory drug, selectively inhibited the growth of replicating, non-replicating and MDR clinical isolates of M. tuberculosis. Objectives We aimed to reveal the whole-cell phenotypic and transcriptomic effects of carprofen in mycobacteria. Methods Integrative molecular and microbiological approaches such as resazurin microtitre plate assay, high-throughput spot-culture growth inhibition assay, whole-cell efflux inhibition, biofilm inhibition and microarray analyses were performed. Analogues of carprofen were also synthesized and assessed for their antimycobacterial activity. Results Carprofen was found to be a bactericidal drug that inhibited mycobacterial drug efflux mechanisms. It also restricted mycobacterial biofilm growth. Transcriptome profiling revealed that carprofen likely acts by targeting respiration through the disruption of membrane potential. The pleiotropic nature of carprofen’s anti-TB action may explain why spontaneous drug-resistant mutants could not be isolated in practice. Conclusions This immunomodulatory drug and its chemical analogues have the potential to reverse TB antimicrobial drug resistance, offering a swift path to clinical trials of novel TB drug combinations

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