Mok, Shaffer

INTRODUCTION: The study aimed to develop consensus recommendations for the safe and appropriate use of liquid nitrogen spray cryotherapy in Barrett's esophagus and esophageal cancer. METHODS: Statements were initially formulated and grouped into categories of indications, training, preprocedure, intraprocedure, and postprocedure recommendations, frequency/timing of repeat procedures, and contraindications. Two investigators assessed the strength of the evidence for each statement using Grading of Recommendations Assessment, Development, and Evaluation methodology. Nineteen experts were invited to participate in a modified Delphi process. For statements that received >80% agreement, minor iterative edits were performed until there was unanimous consensus. If a threshold of 80% agreement was not reached, the statement was modified based on feedback and included in the next Delphi round. Statements were rejected if no consensus was reached after 3 rounds. RESULTS: After 3 Delphi rounds, 41 of 42 statements were accepted either in their original version (n = 11) or after modification based on expert input (n = 30). DISCUSSION: Through a modified Delphi process, we developed expert and evidence-based consensus recommendations to guide safe and appropriate use of liquid nitrogen spray cryotherapy in Barrett's esophagus and esophageal cancer.

Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.

Objective: We report the development and validation of a combined DNA/RNA next-generation sequencing (NGS) platform to improve the evaluation of pancreatic cysts. Background and Aims: Despite a multidisciplinary approach, pancreatic cyst classification, such as a cystic precursor neoplasm, and the detection of high-grade dysplasia and early adenocarcinoma (advanced neoplasia) can be challenging. NGS of preoperative pancreatic cyst fluid improves the clinical evaluation of pancreatic cysts, but the recent identification of novel genomic alterations necessitates the creation of a comprehensive panel and the development of a genomic classifier to integrate the complex molecular results. Methods: An updated and unique 74-gene DNA/RNA-targeted NGS panel (PancreaSeq Genomic Classifier) was created to evaluate 5 classes of genomic alterations to include gene mutations (e.g., KRAS, GNAS, etc.), gene fusions and gene expression. Further, CEA mRNA (CEACAM5) was integrated into the assay using RT-qPCR. Separate multi-institutional cohorts for training (n=108) and validation (n=77) were tested, and diagnostic performance was compared to clinical, imaging, cytopathologic, and guideline data. Results: Upon creation of a genomic classifier system, PancreaSeq GC yielded a 95% sensitivity and 100% specificity for a cystic precursor neoplasm, and the sensitivity and specificity for advanced neoplasia were 82% and 100%, respectively. Associated symptoms, cyst size, duct dilatation, a mural nodule, increasing cyst size, and malignant cytopathology had lower sensitivities (41–59%) and lower specificities (56–96%) for advanced neoplasia. This test also increased the sensitivity of current pancreatic cyst guidelines (IAP/Fukuoka and AGA) by >10% and maintained their inherent specificity. Conclusions: PancreaSeq GC was not only accurate in predicting pancreatic cyst type and advanced neoplasia but also improved the sensitivity of current pancreatic cyst guidelines.