NYU Grossman School of Medicine, 550 1st Ave., New York, NY, 10016, USA.
NYU Langone Department Internal Medicine, Division of Gastroenterology, 240 East 38th Street, 23rd Floor, New York, NY, 10016, USA.
Curr Gastroenterol Rep. 2023 Aug;25(8):182-190. doi: 10.1007/s11894-023-00877-6. Epub 2023 Jul 6.
As abdominal imaging becomes more sensitive and regularly used, pancreatic cystic lesions (PCLs) are being diagnosed more frequently. A small but clinically significant minority of these lesions have a predisposition to either harbor malignancy or undergo malignant transformation. This review highlights the current state and performance of cystic fluid biomarkers and how they may be incorporated into management.
Among the major domains of molecular testing for PCLs, DNA based analyses have demonstrated the highest accuracy in identifying cyst type and have the most data to support their clinical use. However, epigenetic and protein biomarker based molecular assessments have emerged with the potential to complement DNA based approaches. In addition, recent studies have increasingly demonstrated the value associated with combinations of mutations and other biomarkers in identifying higher grade mucinous cystic lesions. We present the performance of individual biomarkers in cyst fluid analysis with an emphasis on an algorithmic approach to improve the accurate identification of both cyst type and risk of malignant transformation.
随着腹部影像学检查变得更加敏感且常规应用,胰腺囊性病变(PCL)的诊断也更加频繁。这些病变中,一小部分具有恶性肿瘤倾向或发生恶性转化。本综述强调了目前囊性液生物标志物的状态和性能,以及它们如何纳入管理。
在 PCL 的主要分子检测领域中,基于 DNA 的分析在识别囊型方面表现出最高的准确性,并且有最多的数据支持其临床应用。然而,基于表观遗传和蛋白质生物标志物的分子评估已经出现,具有补充基于 DNA 方法的潜力。此外,最近的研究越来越多地证明,在识别高级别黏液性囊性病变时,突变和其他生物标志物的组合与相关价值。我们介绍了在囊液分析中使用单个生物标志物的性能,并强调了一种算法方法,以提高对囊型和恶性转化风险的准确识别。