The Use of Clinical Parameters as Adjuncts to Endoscopic Evaluation of Mural Thickening on Conventional Computed Tomography in Diagnosing Malignancy.

BACKGROUND: Mural thickening (MT) on computed tomography (CT) poses a diagnostic dilemma in the absence of clear reporting guidelines. The aim of this study was to analyse CT reports, identifying patients in whom gastrointestinal wall MT was observed, and to correlate these reports with subsequent endoscopic evaluation. METHODS: Patients with MT who had follow-up endoscopy were included in the study (n = 308). The cohort was subdivided into upper gastrointestinal mural thickening (UGIMT) & lower gastrointestinal mural thickening (LGIMT). RESULTS: In total, 55.71% (n = 122) of colonoscopies and 61.8% (n = 55) of gastroscopies were found to be normal. Haemoglobin (HB) level in combination with MT was a predictor of neoplasia in both arms (p = 0.04 UGIMT cohort, p < 0.001 LGIMT cohort). In addition to this, age was a significant correlative parameter in both UGIMT and LGIMT cohorts (p = 0.003, p < 0.001 respectively). Dysphagia and weight loss were associated with UGI malignancies (38 and 63% respectively) and rectal bleeding was correlative in 20% of patients with LGI malignancies. CONCLUSION: HB, advancing age, and red flag symptoms are potentially useful adjuncts to MT in predicting upper and lower gastrointestinal malignancies. We propose the adoption of a streamlined pathway to delineate patients who should undergo endoscopic investigation following CT identification of MT.

A predictive Bayesian network that risk stratifies patients undergoing Barrett's surveillance for personalized risk of developing malignancy.

BACKGROUND: Barrett's esophagus is strongly associated with esophageal adenocarcinoma. Considering costs and risks associated with invasive surveillance endoscopies better methods of risk stratification are required to assist decision-making and move toward more personalised tailoring of Barrett's surveillance. METHODS: A Bayesian network was created by synthesizing data from published studies analysing risk factors for developing adenocarcinoma in Barrett's oesophagus through a two-stage weighting process. RESULTS: Data was synthesized from 114 studies (n = 394,827) to create the Bayesian network, which was validated against a prospectively maintained institutional database (n = 571). Version 1 contained 10 variables (dysplasia, gender, age, Barrett's segment length, statin use, proton pump inhibitor use, BMI, smoking, aspirin and NSAID use) and achieved AUC of 0.61. Version 2 contained 4 variables with the strongest evidence of association with the development of adenocarcinoma in Barrett's (dysplasia, gender, age, Barrett's segment length) and achieved an AUC 0.90. CONCLUSION: This Bayesian network is unique in the way it utilizes published data to translate the existing empirical evidence surrounding the risk of developing adenocarcinoma in Barrett's esophagus to make personalized risk predictions. Further work is required but this tool marks a vital step towards delivering a more personalized approach to Barrett's surveillance.