Molecular analysis of pancreatic cystic neoplasm in routine clinical practice

Abstract

BACKGROUND Cystic pancreatic lesions consist of a wide variety of lesions that are becoming increasingly diagnosed with the growing use of imaging techniques. Of these, mucinous cysts are especially relevant due to their risk of malignancy. However, morphological findings are often suboptimal for their differentiation. Endoscopic ultrasound fine-needle aspiration (EUS-FNA) with molecular analysis has been suggested to improve the diagnosis of pancreatic cysts. AIM To determine the impact of molecular analysis on the detection of mucinous cysts and malignancy. METHODS An 18-month prospective observational study of consecutive patients with pancreatic cystic lesions and an indication for EUS-FNA following European clinical practice guidelines was conducted. These cysts included those > 15 mm with unclear diagnosis, and a change in follow-up or with concerning features in which results might change clinical management. EUS-FNA with cytological, biochemical and glucose and molecular analyses with next-generation sequencing were performed in 36 pancreatic cysts. The cysts were classified as mucinous and non-mucinous by the combination of morphological, cytological and biochemical analyses when surgery was not performed. Malignancy was defined as cytology positive for malignancy, high-grade dysplasia or invasive carcinoma on surgical specimen, clinical or morphological progression, metastasis or death related to neoplastic complications during the 6-mo follow-up period. Next-generation sequencing results were compared for cyst type and malignancy. RESULTS Of the 36 lesions included, 28 (82.4%) were classified as mucinous and 6 (17.6%) as non-mucinous. Furthermore, 5 (13.9%) lesions were classified as malignant. The amount of deoxyribonucleic acid obtained was sufficient for molecular analysis in 25 (69.4%) pancreatic cysts. The amount of intracystic deoxyribonucleic acid was not statistically related to the cyst fluid volume obtained from the lesions. Analysis of KRAS and/or GNAS showed 83.33% [95% confidence interval (CI): 63.34-100] sensitivity, 60% (95%CI: 7.06-100) specificity, 88.24% (95%CI: 69.98-100) positive predictive value and 50% (95%CI: 1.66-98.34) negative predictive value ( P = 0.086) for the diagnosis of mucinous cystic lesions. Mutations in KRAS and GNAS were found in 2/5 (40%) of the lesions classified as non-mucinous, thus recategorizing those lesions as mucinous neoplasms, which would have led to a modification of the follow-up plan in 8% of the cysts in which molecular analysis was successfully performed. All 4 (100%) malignant cysts in which molecular analysis could be performed had mutations in KRAS and/or GNAS , although they were not related to malignancy ( P > 0.05). None of the ...

Advances in the Diagnosis of Pancreatic Cystic Lesions

“…Of all these gene analyses, only KRAS and/or GNAS could distinguish mucinous (from non-mucinous) cysts with a sensitivity of 83.3% and a specificity of 60%. None of the other analyzed mutations could reliably differentiate mucinous cysts or to detect malignant cysts with statistical significance [ 61 ].…”

Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

“…Endoscopic ultrasonography (EUS) is used for diagnosing pancreatic cystic neoplasms (PCN) such as intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), serous cystic neoplasm (SCN), and other cystic cases [1][2][3][4][5][6][7]. As the spectrum of PCN varies widely from benign to malignant, the accurate diagnosis of PCN is required.…”

“…As the spectrum of PCN varies widely from benign to malignant, the accurate diagnosis of PCN is required. While MCN has the potential to develop into a malignancy needing early surgical resection, SCN involves a relatively small risk of malignancy [1,2,5,[7][8][9][10][11][12][13]. Thus, it is critical to determine whether a cyst is MCN or SCN [1,9,13,14], which can be categorized as mucinous and non-mucinous cysts, respectively [1,6,7,10].…”

“…Gaddam et al [7] also tried to find the optimal cutoff value for the CEA level to diagnose between mucinous and non-mucinous cystic lesions, and the result from the study could reach an AUC around 0.77. Pérez et al [5] investigated the impact of molecular analysis for PCN classification by analyzing the combination of morphological, cytological, and biochemical analysis to differentiate between mucinous and non-mucinous PCNs. Their study showed that molecular analysis was effective for the differentiation of mucinous cysts from non-mucinous cysts.…”

“…Various imaging modalities such as endoscopic ultrasound (EUS), magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) are used for visual diagnosis of MCN and SCN [1,[3][4][5]7,10,12]. Wang G. et al [13] proposed a method to differentiate PCNs by combining the observations from PET/CT with multiparametric analysis such as the maximum and mean of standardized uptake values, metabolic tumor volume (MTV), and total lesion glycolysis (TLG).…”