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A location-based anatomic classification system for acute pancreatic fluid collections: Roadmap for optimal intervention in the step-up era.

ABSTRACT: Walled-off pancreatic necrosis (WOPN) is a local complication of acute necrotizing pancreatitis frequently requiring intervention. Treatment is typically through the coordinated efforts of a multidisciplinary team. Current management guidelines recommend a step-up approach beginning with minimally invasive techniques (percutaneous or transmural endoscopic drainage) followed by escalation to more invasive procedures if needed. Although the step-up approach is an evidence-based treatment paradigm for management of pancreatic fluid collections, it lacks guidance regarding optimal invasive technique selection based on the anatomic characteristics of pancreatic fluid collections. Similarly, existing cross-sectional imaging-based classification systems of pancreatic fluid collections have been used to predict disease severity and prognosis; however, none of these systems are designed to guide intervention. We propose a novel classification system which incorporates anatomic characteristics of pancreatic fluid collections (location and presence of disconnected pancreatic duct) to guide intervention selection and clinical decision making. We believe adoption of this simple classification system will help streamline treatment algorithms and facilitate cross-study comparisons for pancreatic fluid collections.

SUBMITTER: Clark CJ

PROVIDER: S-EPMC10831243 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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A location-based anatomic classification system for acute pancreatic fluid collections: Roadmap for optimal intervention in the step-up era.

Clark Clancy J CJ Ray Jonathan W JW Pawa Swati S Jahann Darius D McCullough MaryAlyce M Miller Preston P Mowery Nathan N Miller Michael M Xiao Ted T Koutlas Nicholas N Pawa Rishi R

Surgery open science 20240115

Walled-off pancreatic necrosis (WOPN) is a local complication of acute necrotizing pancreatitis frequently requiring intervention. Treatment is typically through the coordinated efforts of a multidisciplinary team. Current management guidelines recommend a step-up approach beginning with minimally invasive techniques (percutaneous or transmural endoscopic drainage) followed by escalation to more invasive procedures if needed. Although the step-up approach is an evidence-based treatment paradigm ...[more]

PMID: 38312303

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Project description:BackgroundColorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location.ResultsWe performed network analyses of 54 biomarkers, including major mutational events, microsatellite instability (MSI), epigenetic features, protein expression status, and immune reactions using data from 1380 colorectal cancer cases: 690 cases with proximal colon cancer and 690 cases with distal colorectal cancer matched by age and sex. Edges were defined by statistically significant correlations between biomarkers using Spearman correlation analyses. We found that the proximal colon cancer network formed a denser network (total number of edges, n = 173) than the distal colorectal cancer network (n = 95) (P < 0.0001 in permutation tests). The value of the average clustering coefficient was 0.50 in the proximal colon cancer network and 0.30 in the distal colorectal cancer network, indicating the greater clustering tendency of the proximal colon cancer network. In particular, MSI was a key hub, highly connected with other biomarkers in proximal colon cancer, but not in distal colorectal cancer. Among patients with non-MSI-high cancer, BRAF mutation status emerged as a distinct marker with higher connectivity in the network of proximal colon cancer, but not in distal colorectal cancer.ConclusionIn proximal colon cancer, tumor biomarkers tended to be correlated with each other, and MSI and BRAF mutation functioned as key molecular characteristics during the carcinogenesis. Our findings highlight the importance of considering multiple correlated pathways for therapeutic targets especially in proximal colon cancer.

Project description:BackgroundThe current N classification, which is determined by the anatomical location of positive lymph nodes, does not effectively stratify N1 and N2 non-small cell lung cancer (NSCLC) patients into prognostically significant subgroups.MethodsWe acquired the clinical data of 3,234 N1 and N2 NSCLC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015). We eliminated patients undergoing chemotherapy or radiation because chemotherapy and radiotherapy might lower lymph node stage, and the SEER database does not distinguish between therapy administered before and after surgery. We developed the N-new classification based on the former N stage, the number and ratio of lymph nodes. Patients were finally classified into four categories (N1a, N1b, N2a, N2b). Then, the N-new classification was validated in subgroups based on a variety of clinical characteristics, such as tumor size. The multivariable Cox regression analysis, the decision curve analysis (DCA) and the time-dependent receiver operating characteristic (ROC) analysis were conducted to compare the performance of the N-new classification and the current N classification.ResultsThe cancer-specific survival (CSS) and overall survival were significantly different among each pair of N-new classification. And the same results were shown in the majority of the subgroups determined by various clinical characteristics. Compared with the current N classification (C-index, 0.639), the N-new classification (C-index, 0.652) performed better in classifying N1 and N2 NSCLC patients into subgroups with distinctive clinical outcomes. The 5-year CSS rates were 49.7%, 41.4%, 30.4% and 20.4% for N1a, N1b, N2a and N2b, respectively.ConclusionsWhen compared to the current N classification, the N-new classification could be a more reliable and accurate prognostic determinant, which is worth considering in the revision of the 9th edition of the tumor, node, metastasis (TNM) staging system.

Dataset Information

A location-based anatomic classification system for acute pancreatic fluid collections: Roadmap for optimal intervention in the step-up era.

Publications

A location-based anatomic classification system for acute pancreatic fluid collections: Roadmap for optimal intervention in the step-up era.

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