New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Efficiency in colonoscopy

Global trends, including demographic changes, are significantly increasing the demand and cost of healthcare. Endoscopy services are no exception and, even before the Covid-19 pandemic, significant pressure resulted in many units failing to meet cancer wait targets. The need to improve efficiency has never been greater and particularly so for colonoscopy which significantly reduces morbidity and mortality from colorectal cancer. Today, advances in colonoscope technologies and emergence of artificial intelligence offer the potential for improved colonoscopy practice. The aim of this thesis is to explore how efficiency in colonoscopy can be enhanced throughout the patient pathway. Five major studies were performed evaluating bowel preparation (CLEANSE), polyp detection (AI-DETECT), optical diagnosis (DISCARD3), insertion technique (WAVE) and post-colonoscopy colorectal cancer (AI-DETECT). CLEANSE is an evaluation of a novel low-volume same-day bowel preparation regime (Plenvu) and showed this offers a more efficient bowel cleansing option than standard regimens. AI-DETECT is a randomised study evaluating a computer-aided detection (CADe) system (GI Genius) and showed a borderline significant improvement in polyp detection is achieved amongst high performing endoscopists. DISCARD3 is a major evaluation of optical diagnosis with a “resect and discard” strategy exploring the learning curve, quality assurance process, causes of error and economic impact. This study shows such a strategy is feasible and safe and could potentially be implemented with a quality assurance process in place within the English Bowel Cancer Screening Progamme (BCSP). WAVE is a randomised study evaluating colonoscopy insertion technique. This showed a ‘hybrid’ insertion technique is more efficient than a water-exchange colonoscopy technique. REFLECT is a retrospective evaluation of post-colonoscopy colorectal cancer cases identified at national level and showed after local root cause analysis a significant proportion were in fact detected cancers. These studies provide valuable insights that we hope will ultimately lead to more efficient colonoscopy whilst maintaining quality and enhancing patient care.Open Acces

Examining lipid metabolism of colorectal adenomas and carcinomas using Rapid Evaporative Ionisation Mass Spectrometry (REIMS)

Background There is an unmet need for real-time intraoperative colorectal tissue recognition, which would promote personalised oncologic decision making. Rapid Evaporative Ionization Mass Spectrometry (REIMS) analyses the composition of cellular lipids through the aerosol generated from electrosurgical instruments, providing a novel diagnostic platform and surgeon feedback. Thesis Hypothesis Colorectal lipid metabolism and cellular lipid composition are associated with the phenotype of colorectal adenomas and carcinomas, which can be leveraged for tissue recognition in vivo. Methods This thesis contains three work packages. First, a method for REIMS spectral quality control was developed based on a human dataset and analysis of a porcine model assessed the spectral impact of technical and environmental factors. Second, an ex vivo spectral reference database was constructed from analysis of human colorectal tissues, assessing the ability of REIMS for tissue recognition. Finally, REIMS was translated into the operating theatre, for proof-of-principle application of during transanal minimally invasive surgery (TAMIS). Results Sensitivity analyses revealed seven minimum quality criteria for REIMS spectra to be included in all future statistical analyses, with quality also impacted by low diathermy power, coagulation mode and tissue contamination. Based on tissue of 161 patients, REIMS could differentiate colorectal normal, adenoma and cancer tissue with 91.1% accuracy, and disease from normal with 93.5% accuracy. REIMS could risk-stratify adenomas by predicting grade of dysplasia, however not histological features of poor prognosis in cancers. 61 pertinent lipid metabolites were structurally identified. REIMS was coupled to TAMIS in seven patients. Optimisation of the workflow successfully increased signal intensity, with tissue recognition showing high accuracy in vivo and identification of a cancer-involved margin. Discussion This thesis demonstrates that REIMS can be optimised and applied for accurate real-time colorectal tissue recognition based on cellular lipid composition. This can be translated in vivo, with promising results during first-in-man mass spectrometry-coupled TAMIS.Open Acces