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

Deep learning biopsy marking of early neoplasia in barrett's esophagus by combining wle and BLI modalities

\u3cp\u3eEsophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett's esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%.\u3c/p\u3

Deep learning biopsy marking of early neoplasia in barrett's esophagus by combining wle and BLI modalities

Esophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett's esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%