Leggett, Cadman L

INTRODUCTION: Barrett esophagus (BE) is the strongest known risk factor for developing esophageal adenocarcinoma (EAC), the second-most lethal cancer in the United States. Esopredict is a novel validated methylation-based biomarker assay that provides precise quantification of neoplastic progression risk in BE patients. Inherit challenges, including tissue heterogeneity, sampling error, interobserver variability, and inconsistent adherence to surveillance biopsy guidelines, may affect the predictive value results of Esopredict obtained at different anatomic locations or different sampling time points. METHODS: To investigate the spatiotemporal performance of Esopredict across multiple spatiotemporal sampling points, we profiled 220 biopsies obtained from 58 BE patients, including 11 patients with overlapping spatial and temporal biopsies. We focused on spatial profiling (i.e., multiple biopsies obtained at several anatomic locations during a single endoscopy) and temporal profiling (i.e., biopsies obtained from multiple endoscopies performed at different time points). Each patient had an initial histologic diagnosis of nondysplastic Barrett esophagus, indefinite for dysplasia, or low-grade dysplasia. Final follow-up (endpoint) biopsies showed either high-grade dysplasia or EAC (progressors), or nondysplastic Barrett esophagus, indefinite for dysplasia, or low-grade dysplasia (nonprogressors). Biopsies were analyzed with Esopredict to compute a progression risk score, which quantified the likelihood of future progression to high-grade dysplasia or EAC within 5 years. RESULTS: In 52 spatially profiled patients, Esopredict demonstrated a sensitivity of 81% (17/21 progressor patients), based on the highest-scoring biopsy from each patient; sensitivity increased to 100% (12/12) when end point biopsies occurred within 5 years of the index (initial) biopsy. In 28 temporally profiled patients, sensitivity was 100% (8/8 patients), based on the biopsy performed at the time point closest to the end point biopsy. DISCUSSION: Esopredict showed high predictive performance in multiple spatiotemporal samples in BE patients. These data further support the use of Esopredict as a robust test to distinguish high-risk BE patients, who may benefit from endoscopic eradication therapy or increased surveillance frequency, from low-risk patients, who may be candidates for less frequent surveillance and noninterventional observation.

INTRODUCTION: Esophageal adenocarcinoma (EAC) is the second-most lethal cancer in the United States, with Barrett esophagus (BE) being the strongest risk factor. Assessing the future risk of neoplastic progression in patients with BE is difficult; however, high-grade dysplasia (HGD) and early EAC are treatable by endoscopic eradication therapy (EET), with survival rates of 90%. Thus, it would be beneficial to develop a molecular assay to identify high-risk patients, who merit more frequent endoscopic surveillance or EET, as well as low-risk patients, who can avoid EET and undergo less frequent surveillance. METHODS: Deidentified endoscopic biopsies were acquired from 240 patients with BE at 6 centers and confirmed as future progressors or nonprogressors. Tissues were analyzed by a set of methylation-specific biomarker assays. Test performance was assessed in an independent validation set using 4 stratification levels: low risks, low-moderate risks, high-moderate risks, and high risks. RESULTS: Relative to patients in the low-risk group, high-risk patients were 15.2 times more likely to progress within 5 years to HGD or EAC. For patients in the high-risk category, the average risk of progressing to HGD or EAC within 5 years was 21.5%, 4-fold the BE population prevalence within 5 years, whereas low-risk patients had a progression risk of only 1.85%. DISCUSSION: This clinical assay, Esopredict, stratifies future neoplastic progression risk to identify higher-risk patients with BE who can benefit from EET or more frequent surveillance and lower-risk patients who can benefit from reduced surveillance.

INTRODUCTION: Endoscopic eradication therapy (EET) combining endoscopic resection (ER) with endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) followed by ablation is the standard of care for the treatment of dysplastic Barrett's esophagus (BE). We have previously shown comparable rates of complete remission of intestinal metaplasia (CRIM) with both approaches. However, data comparing recurrence after CRIM are lacking. We compared rates of recurrence after CRIM with both techniques in a multicenter cohort. METHODS: Patients undergoing EET achieving CRIM at 3 academic institutions were included. Demographic and clinical data were abstracted. Outcomes included rates and predictors of any BE and dysplastic BE recurrence in the 2 groups. Cox-proportional hazards models and inverse probability treatment weighting (IPTW) analysis were used for analysis. RESULTS: A total of 621 patients (514 EMR and 107 ESD) achieving CRIM were included in the recurrence analysis. The incidence of any BE (15.7, 5.7 per 100 patient-years) and dysplastic BE recurrence (7.3, 5.3 per 100 patient-years) were comparable in the EMR and ESD groups, respectively. On multivariable analyses, the chances of BE recurrence were not influenced by ER technique (hazard ratio 0.87; 95% confidence interval 0.51–1.49; P = 0.62), which was also confirmed by IPTW analysis (ESD vs EMR: hazard ratio 0.98; 95% confidence interval 0.56–1.73; P = 0.94). BE length, lesion size, and history of cigarette smoking were independent predictors of BE recurrence. DISCUSSION: Patients with BE dysplasia/neoplasia achieving CRIM, initially treated with EMR/ablation, had comparable recurrence rates to ESD/ablation. Randomized trials are needed to confirm these outcomes between the 2 ER techniques.

INTRODUCTION: Endoscopic eradication therapy (EET) is standard of care for T1a esophageal adenocarcinoma (EAC). However, data on outcomes in high-risk T1a EAC are limited. We assessed and compared outcomes after EET of low-risk and high-risk T1a EAC, including intraluminal EAC recurrence, extraesophageal metastases, and overall survival. METHODS: Patients who underwent EET for T1a EAC at 3 referral Barrett's esophagus endotherapy units between 1996 and 2022 were included. Patients with submucosal invasion, positive deep margins, or metastases at initial diagnosis were excluded. High-risk T1a EAC was defined as T1a EAC with poor differentiation and/or lymphovascular invasion, with low-risk disease being defined without these features. All pathology was systematically assessed by expert gastrointestinal pathologists. Baseline and follow-up endoscopy and pathology data were abstracted. Time-to-event analyses were performed to compare outcomes between groups. RESULTS: One hundred eighty-eight patients with T1a EAC were included (high risk, n = 45; low risk, n = 143) with a median age of 70 years, and 84% were men. Groups were comparable for age, sex, Barrett's esophagus length, lesion size, and EET technique. Rates of delayed extraesophageal metastases (11.1% vs 1.4%) were significantly higher in the high-risk group ( P = 0.02). There was no significant difference in the rates of intraluminal EAC recurrence ( P = 0.79) and overall survival ( P = 0.73) between the 2 groups. DISCUSSION: Patients with high-risk T1a EAC undergoing successful EET had a substantially higher rate of extraesophageal metastases compared with those with low-risk T1a EAC on long-term follow-up. These data should be factored into discussions with patients while selecting treatment approaches. Additional prospective data in this area are critical.

INTRODUCTION: Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS: The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS: We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION: ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

Abstract Designing an effective antenna is very important to utilize microwave imaging. A high gain antipodal antenna designed with ultra-wideband frequency range is presented here. The simulated results are validated with measured data. The antenna can operate within the Ultra band frequency range of 3 to 8 GHz. The maximum gain of 2.96 dBi is obtained at 4 GHz. An experiment with biological tissues is also done to show the effectiveness of the antenna using as a resonator.

Abstract Optical coherence tomography (OCT) is an advanced imaging modality to detect Barrett’s esophagus (BE) dysplasia, providing widefield, cross-sectional imaging and microscopic resolution. BE dysplasia is characterized under OCT by the presence and number of glandular structures with atypical morphology. Accurate detection and interpretation of BE glands under OCT is essential to detect dysplastic lesions. Object Detection using deep learning has the potential to identify glands from OCT images. We developed a YOLO model to identify the presence of glands in BE tissue. The YOLOv4 object detector was trained on a custom BE dataset of 30 patients with confirmed BE who underwent OCT imaging, of which 222 OCT images included at least one gland. Our model identified glands with a high average precision of 88.79% on the test dataset. We showed that the developed model is robust to rotation, brightness, and blur in images. We have implemented an object detection model to identify glands from OCT images with promising results accurately. This model has the potential to improve the diagnosis and surveillance of BE by eliminating human error and missed dysplastic lesions adaptable for capsule endoscopy applications.