Mucinous Pancreatic Cystic Lesions Research Articles

Abstract B037: Disks large-associated protein 5 (DLGAP5) is a putative prognostic biomarker for intraductal papillary mucinous neoplasm (IPMN) of pancreas

Abstract Pancreatic cancer is one of the deadliest malignant diseases, accounting for nearly 500,000 deaths worldwide each year. One of the pancreatic cancer precursors are mucinous pancreatic cystic lesions (PCL). Although only a minority of these PCLs undergo malignant transformation, the clinical significance is considerable, particularly given the prevalence of up to 45% in individuals over the age of 65. The most prevalent type of neoplastic PCL is intraductal papillary mucinous neoplasm (IPMN) and the management of different IPMN grades varies, with high- grade dysplasia and invasive carcinoma being indicated for surgery. However, the current guidelines for patient stratification are limited and often lead to either overtreatment or undertreatment, both of which are associated with high mortality and morbidity. Therefore, there is a pressing need to identify biomarkers associated with progression of IPMN from low to high grade that can be used for more accurate lesion assessment. Here we identified a novel biomarker highly correlated with IPMN progression – DLGAP5. Both mRNA as well as protein expression of DLGAP5 is positively associated with increased grade of IPMNs as determined by analysis of patient derived tumor samples. Additionally, in vitro experiments confirmed that knock down of DLGAP5 in pancreatic cancer cells leads to reduction of cell proliferation, and conversely, overexpression of DLGAP5 in IPMN precursor cells resulted in increased proliferation. Since one of the functions of DLGAP5 is microtubule stabilization, we hypothesized that the secretome of IPMN precursor cells will be altered by the expression of DLGAP5. Indeed, our mass spectrometry-based analysis of secreted proteins identified several hits (e.g. DKK1) that were correlated with DLGAP5 expression and were previously confirmed to be present in IPMN cyst fluid in patients. Subsequently, we aim to validate the correlation of the putative biomarkers in IPMN cyst fluid and serum with IPMN grade and patients’ clinical outcome in a prospective manner. To sum up, we validated DLGAP5 as an IPMN-progression biomarker and identified several secreted proteins that could serve as DLGAP5-surrogate biomarkers for stratification of the patients with IPMN and subsequent treatment optimalization. Citation Format: Radoslav Janostiak, Peter Mačinga, Lucia Csergeová, Ondřej Fabián, Eva Sticová, Jiří Zahradník, Martin Květoň, Tomáš Hucl. Disks large-associated protein 5 (DLGAP5) is a putative prognostic biomarker for intraductal papillary mucinous neoplasm (IPMN) of pancreas [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B037.

Neutrophil Gelatinase-Associated Lipocalin for the Differentiation of Mucinous Pancreatic Cystic Lesions.

Undetermined pancreatic cystic lesion (PCL) differentiation benefits from endoscopic ultrasound (EUS) based on morphology and cyst fluid analysis, but room for new biomarkers exists. Our aim was to assess the intracystic and serum diagnostic value of neutrophil gelatinase-associated lipocalin (Ngal) and interleukin 1 beta (IL-1β) for differentiation of PCLs. This prospective study included patients from one tertiary hospital, evaluated between April 2018 and May 2020. EUS fine-needle aspiration or pancreatic pseudocysts drainage was the source of PCL intracystic liquid. The final diagnosis was based on surgery or EUS results (morphology, cytology, glucose, and CEA-carcinoembryogenic antigen). The intracystic samples were tested for Ngal, IL-1β, glucose, and CEA, and serum for Ngal and IL-1β. We evaluated 63 cysts, 33 pseudocysts, and 30 non-inflammatory cysts. The diagnostic sensitivity and specificity for mucinous PCL was 70.8% and 92.3% for intracystic Ngal (cut-off: 500-800 ng/dL), without correlation with serum Ngal, no matter the inclusion of infected pseudocysts. After exclusion of infected pseudocysts, the sensitivity and specificity for glucose were 87% and 75%, respectively, and for CEA, they were 87.1%, and 96.8%, respectively. Intracystic Ngal shows promise in differentiating mucinous PCLs, but researchers need to conduct further studies to confirm its effectiveness. Intracystic IL-1β and serum Ngal made no diagnostic contribution.

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.

Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy.

Accurate classification of pancreatic cystic lesions (PCLs) is important to facilitate proper treatment and to improve patient outcomes. We utilized the convolutional neural network (CNN) of VGG19 to develop a computer-aided diagnosis (CAD) system in the classification of subtypes of PCLs in endoscopic ultrasound-guided needle-based confocal laser endomicroscopy (nCLE). From a retrospectively collected 22,424 nCLE video frames (50 videos) as the training/validation set and 11,047 nCLE video frames (18 videos) as the test set, we developed and compared the diagnostic performance of three CNNs with distinct methods of designating the region of interest. The diagnostic accuracy for subtypes of PCLs by CNNs with manual, maximal rectangular, and U-Net algorithm-designated ROIs was 100%, 38.9%, and 66.7% on a per-video basis and 88.99%, 73.94%, and 76.12% on a per-frame basis, respectively. Our per-frame analysis suggested differential levels of diagnostic accuracy among the five subtypes of PCLs, where non-mucinous PCLs (serous cystic neoplasm: 93.11%, cystic neuroendocrine tumor: 84.31%, and pseudocyst: 98%) had higher diagnostic accuracy than mucinous PCLs (intraductal papillary mucinous neoplasm: 84.43% and mucinous cystic neoplasm: 86.1%). Our CNN demonstrated superior specificity compared to the state-of-the-art for the classification of mucinous PCLs (IPMN and MCN), with high specificity (94.3% and 92.8%, respectively) but low sensitivity (46% and 45.2%, respectively). This suggests the complimentary role of CNN-enabled CAD systems, especially for clinically suspected mucinous PCLs.

Early phase trial of intracystic injection of large surface area microparticle paclitaxel for treatment of mucinous pancreatic cysts.

Background and study aims Mucinous pancreatic cystic lesions (PCLs) have the potential for malignant transformation, for which the only accepted curative modality is surgery. A novel intracystic therapy with large surface area microparticle paclitaxel (LSAM-PTX) may treat PCLs without local or systemic toxicities. Safety and preliminary efficacy of LSAM-PTX for the treatment of PCLs administered by endoscopic ultrasound-guided fine-needle injection (EUS-FNI) was evaluated. Patients and methods Ten subjects with confirmed PCLs (size > 1.5 cm) received intracystic LSAM-PTX via EUS-FNI at volumes equal to those aspirated from the cyst in sequential cohorts at 6, 10, and 15 mg/mL in a standard "3 + 3" dose-escalation protocol. The highest dose with acceptable safety and tolerability was taken into the confirmatory phase where nine additional subjects received two injections of LSAM-PTX 12 weeks apart. Subjects were followed for 6 months after initial LSAM-PTX treatment for endpoints including: adverse events (AEs), tolerability, pharmacokinetic analysis of systemic paclitaxel drug levels, and change in cyst volume. Results Nineteen subjects completed the study. No dose-limiting toxicities, treatment-related serious AEs, or clinically significant laboratory changes were reported. Systemic paclitaxel concentrations did not exceed 3.5 ng/mL at any timepoint measured and fell below 1 ng/mL by Week 2, supporting the lack of systemic toxicity. By Week 24 a cyst volume reduction (10-78 %) was seen in 70.6 % of subjects. Conclusions Intracystic injection of LSAM-PTX into mucinous PCLs resulted in no significant AEs, a lack of systemic absorption, and resulted in reduction of cyst volume over a 6month period.

Accurate Identification of Mucinous Pancreatic Cystic Lesions Using Small-Volume Analytes

IntroductionThe accurate identification of mucinous pancreatic cystic lesions (PCLs) is paramount for cancer risk stratification. Cyst fluid carcinoembryonic antigen (CEA), the only routinely used test, requires high volumes and has low sensitivity. We aimed to compare the performance of two investigational small-volume biomarkers, glucose and the protease gastricsin, to CEA for PCL classification. MethodsWe obtained cyst fluid samples from 81 patients with pathologically confirmed PCLs from four institutions between 2003 and 2016. Gastricsin activity was measured using an internally quenched fluorescent substrate. Glucose levels were measured with a standard glucometer. CEA levels were obtained from the medical record. Models using Classification and Regression Trees were created to predict mucinous status. Model performance was evaluated using nested cross-validation. ResultsGastricsin activity, CEA, and glucose levels from patients with mucinous (n = 50) and nonmucinous (n = 31) PCLs were analyzed. Area under the curve (AUC) was similar for individual classifiers (gastricsin volume normalized [GVN] 0.88; gastricsin protein concentration normalized [GPN] 0.95; glucose 0.83; CEA 0.84). The combination of two classifiers did not significantly improve AUC, with CEA + GVN (0.88) performing similarly to CEA + GPN (0.95), GVN + glucose (0.87), GPN + glucose (0.95), and CEA + glucose (0.84). The three-analyte combination performed similarly to single and dual classifiers (GPN + glucose + CEA AUC 0.95; GVN + glucose + CEA AUC 0.87). After multiple comparison corrections, there were no significant differences between the individual, dual, and triple classifiers. ConclusionsGastricsin and glucose performed similarly to CEA and required <5% of the volume required for CEA; these classifiers may be useful in patients with limited cyst fluid. Future multicenter prospective studies are needed to validate and compare these novel small-volume biomarkers.

Assessment of independent predictors for high-grade dysplasia or adenocarcinoma and the long-term mortality in patients with mucinous pancreatic cystic lesions

Assessment of independent predictors for high-grade dysplasia or adenocarcinoma and the long-term mortality in patients with mucinous pancreatic cystic lesions

Comparative Performance of Endoscopic Ultrasound-Based Techniques in Patients With Pancreatic Cystic Lesions: A Network Meta-Analysis.

Evidence on the comparative diagnostic performance of endoscopic ultrasound (EUS)-based techniques for pancreatic cystic lesions (PCLs) is limited. This network meta-analysis comprehensively compared EUS-based techniques for PCL diagnosis. A comprehensive literature search was performed for all comparative studies assessing the accuracy of 2 or more modalities for PCL diagnosis. The primary outcome was the diagnostic efficacy for mucinous PCLs. Secondary outcomes were the diagnostic efficacy for malignant PCLs, diagnostic success rate, and adverse event rate. A network meta-analysis was conducted using the ANOVA model to assess the diagnostic accuracy of each index. Forty studies comprising 3,641 patients were identified. The network ranking of the superiority index for EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) and EUS-guided through-the-needle biopsy (EUS-TTNB) were significantly higher than other techniques for differentiating mucinous PCLs; besides, EUS-TTNB was also the optimal technique in identifying malignant PCLs. The evidence was inadequate for EUS-nCLE diagnosing malignant PCLs and contrast-enhanced harmonic EUS diagnosing both mucinous and malignant PCLs. Glucose showed a high sensitivity but low specificity, and molecular analysis (KRAS, GNAS, and KRAS + GNAS mutations) showed a high specificity but low sensitivity for diagnosing mucinous PCLs. Satisfactory results were not obtained during the evaluation of the efficiency of pancreatic cyst fluid (PCF) biomarkers in detecting malignant PCLs. For centers with relevant expertise and facilities, EUS-TTNB and EUS-nCLE were better choices for the diagnosis of PCLs. Further studies are urgently required for further improving PCF biomarkers and validating the diagnostic performance of the index techniques.

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Lower cyst fluid carcinoembryonic antigencutoff is helpful in the differential diagnosis of mucinous versus non-mucinous pancreatic cysts.

Pancreatic cystic lesions (PCLs) are being diagnosed with increased frequency and have varying neoplastic potential. We conducted this multimodal, prospective study to evaluate the role of tumor cytology and molecular markers to differentiate PCL subtypes. Consecutive undiagnosed patients with PCLs (n = 100, mean age: 50.37 years; 41% males) were prospectively studied. Cyst fluid carcinoembryonic antigen (CEA), CA19.9, CA125, CA72.4, and vascular endothelial growth factor-alpha (VEGF-α) levels were measured by quantitative enzyme-linked immunosorbent assay (ELISA) method. Mutational analysis of the KRAS gene (exon 2, Codon 12 and 13) and GNAS gene (Exon 8, Codon 201) were performed by Sanger's sequencing. The mean cyst size was 4.32 ± 2.4 cm. Fluid cytology revealed definitive diagnosis in 21 (22.3%) patients. All malignant PCLs could be identified on cytology whereas 10/14 (71%) non-malignant mucinous PCLs could also be identified on cytology based on mucin staining. Among the tested tumor markers, cyst fluid CEA had the best diagnostic performance for differentiation between mucinous and non-mucinous PCLs (AUC 0.933 [95% CI 0.86-0.91]). At a cyst fluid CEA cutoff level of 45.0 ng/mL, the sensitivity, specificity, positive predictive value, and negative predictive value for differentiation between mucinous and non-mucinous cysts were 88.5%, 96.8%, 92.0%, and 95.3%, respectively (p &lt; 0.05). KRAS and GNAS mutation had no significant diagnostic benefit in comparison to fluid cytology and CEA levels. Fluid CEA at a lower cutoff of 45 ng/mL is the most accurate marker to differentiate between mucinous and non-mucinous PCL. The KRAS and GNAS mutational analysis does not improve upon the diagnostic performance of fluid cytology and tumor markers.

Abstract 2226: Using functional biomarkers to accurately predict advanced neoplasia in pancreatic cystic lesions

Abstract Background: The accurate identification of high-grade dysplasia (HGD) or invasive cancer (advanced neoplasia, AN) in pancreatic cystic lesions (PCLs) is needed to identify PCLs that warrant surgical intervention. Cytologic evaluation of cyst fluid is widely-used but has poor sensitivity. We previously found that activity of the lysosomal serine protease tripeptidyl peptidase 1 (TPP1) is associated with mucinous PCLs that harbor AN (AUC 0.72). We aimed to identify additional functional biomarkers that would improve performance of our classifier for dysplastic grade in mucinous PCLs. Methods: We used a combination of global protease activity profiling and shotgun proteomics to identify differentiating markers between PCLs with low-grade dysplasia (LGD, n=3) and HGD (n=3). Candidate biomarkers were validated in an independent cohort of 28 clinically-annotated mucinous PCLs (HGD n=12, LGD n=16), by measuring both protein concentration (using ELISA) and enzymatic activity (using internally-quenched fluorescent substrates). We used a nested cross-validation approach to iteratively split our cohort into training and validation sets to predict HGD. Model accuracy was evaluated using area under the curve (AUC), sensitivity, specificity, and accuracy. Results: We identified 8 proteins that were highly abundant in mucinous PCLs with HGD. Among these were the inflammatory enzymes myeloperoxidase (MPO) and neutrophil elastase (ELANE), suggesting that differential activity levels could be exploited that would minimize fluid requirements and cost. Among 28 mucinous PCLs, a composite score that included both activity and mass of MPO performed the best for HGD in mucinous cysts (AUC 0.955, sensitivity 98%, specificity 93%). ELANE activity (AUC 0.76, sensitivity 69%, specificity 59%) and TPP1 activity (AUC 0.765, sensitivity 71%, specificity 74%) had modest performance. Conclusions: Our functional biomarkers accurately classified HGD among mucinous cysts. Functional biomarkers that require low sample volumes (&amp;lt;10μl) of cyst fluid have the potential to substantially improve clinical decision-making for patients with PCLs. Citation Format: Francesco Caiazza, Andre Lourenco, Patricia Conroy, Thomas Hoffmann, Sam L. Ivry, Tyler York, Gina Zhu, Audrey Mustoe, Anthony J. O'Donoghue, Charles S. Craik, Kimberly Kirkwood. Using functional biomarkers to accurately predict advanced neoplasia in pancreatic cystic lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2226.

Abstract 2522: Pathway and immune profile analysis of cyst-derived versus PanIN-derived pancreatic ductal adenocarcinomas

Abstract Introduction: Pancreatic cystic lesions (PCL) are common and a subset of mucinous cysts will transform into pancreatic ductal adenocarcinoma (PDAC). However, predicting which of these mucinous PCL may progress to PDAC and warrant surgery remains a clinical challenge. Moreover, identifying those clinically emergent mucinous PCL for which a surveillance approach is best is a dire clinical need. Therefore, we aimed to identify molecular signatures that distinguished between PDAC with and without clinical evidence of a PCL to identify novel biomarkers. Methods: We leveraged data from the Oncology Research Information Exchange Network (ORIEN) multi-institute sequencing project and analyzed 66 PDAC cases recruited to ORIEN from The Ohio State University Wexner Medical Center and Moffitt Cancer Center for which tumor whole transcriptome sequencing datasets were generated. We separated the cases based on whether a tumor had originated from a cystic lesion (n=16) or presumably through the pancreatic intraepithelial neoplasia (PanIN) pathway (n=50). We then performed differential expression and pathway analysis using both Gene-Set Enrichment Analysis (GSEA) and Pathway Analysis with Down-weighted Genes (PADOG) algorithms. Based on the emerging importance of the immune landscape in PDAC development, we also analyzed immune profiles using a novel tool, Tumor-immune Microenvironment Deconvolution Web-portal for Bulk Transcriptomics (TIMEx). Results: When grouped by tumor origin, cyst-derived PDAC gene expression sets are enriched in immune signaling pathways, specifically NOTCH signaling (p=0.04), and demonstrate significant downregulation in amino acid metabolism, mitochondrial import and Gsα signaling pathways. Furthermore, GSEA based on TIMEx signatures indicated that multiple immune cell-specific profiles had significant enrichment scores in either the cyst-derived (for example, plasma cell: normalized enrichment score=-1.53; p=0.007) or non-cyst-derived (for example, neutrophil: normalized enrichment score=2.24; p=0.0001) PDAC cohorts. Conclusions: Our data suggest that cyst-derived and non-cyst-derived PDACs differ by immune profile, enhanced NOTCH pathway usage and in the metabolic processing of multiple amino acids. These initial findings support future studies to assess the accuracy of risk stratifying PCLs based on their amino acid, metabolic, or immune profiles, and exploration into mechanisms to explain these findings. Citation Format: Margaret A. Park, Somashekar G. Krishna, Maria C. Genilo-Delgado, Kristyn Gumpper-Fedus, Darwin L. Conwell, Phil A. Hart, Mary E. Dillhoff, Maria F. Gomez, Toni L. Basinski, Aamir N. Dam, Jason B. Klapman, Jason B. Fleming, Mokenge Malafa, Amir Mohammadi, Barbara A. Centeno, Kun Jiang, Daniel Jeong, Dung-Tsa Chen, Mengyu Xie, Aik Choon Tan, Brooke L. Fridley, Jamie K. Teer, Zobeida Cruz-Monserrate, Jennifer B. Permuth. Pathway and immune profile analysis of cyst-derived versus PanIN-derived pancreatic ductal adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2522.

Co-analysis of pancreatic cyst fluid carcinoembryonic antigen and glucose with novel cut-off levels better distinguishes between mucinous and non-mucinous neoplastic pancreatic cystic lesions.

Pancreatic cyst fluid analysis plays an important role in distinguishing between mucinous and non-mucinous cyst lesions. We aimed to compare the diagnostic performances of cyst fluid carcinoembryonic antigen (CEA), CA 19-9, and glucose in differentiating mucinous from non-mucinous neoplastic pancreatic cystic lesions (PCLs) and determine the best cut-off levels. Patients' data were evaluated retrospectively. 102 patients' PCLs were grouped as non-neoplastic (n = 25), non-mucinous neoplastic (n = 20), mucinous neoplastic (n = 47) and pancreatic adenocarcinomas with cystic degeneration (n = 10); and CEA, CA 19-9, and glucose levels were compared. Receiver-operating characteristic analysis was performed, and the ideal cut-off values were determined. Cyst fluid CEA and CA 19-9, levels were significantly higher (P < 0.001, P < 0.001, respectively) and glucose levels were significantly lower (P = 0.001) in mucinous than in non-mucinous neoplastic PCLs. Area under curve with 95% confidence interval of CEA, glucose and CEA and glucose test combination was 0.939 (95% CI = 0.885-0.993, P = 0.001), 0.809 (95% CI = 0.695-0.924, P < 0.001) and 0.937 (95% CI = 0.879-0.995), respectively. CEA cut-offs to rule-in and rule-out mucinous neoplastic were 135.1ng/mL (sensitivity = 62%, specificity = 94.7%) and 6.12ng/mL (sensitivity = 94.1%, specificity = 80.4%), respectively. Glucose cut-off of 2.8mmol/L was chosen both to rule-in and rule-out mucinous neoplastic PCLs (sensitivity = 78%, specificity = 80%). Co-analysis of CEA and glucose to distinguish mucinous from non-mucinous neoplastic PCLs had sensitivity = 87.8%, specificity = 93.3%, and diagnostic accuracy = 89.3%. We concluded that co-analysis of cyst fluid CEA (cut-off = 135.1ng/mL) and glucose (cut-off = 2.8mmol/L) at novel cut-offs had the best testing performance to rule-in mucinous neoplastic PCLs. To rule-out mucinous PCLs co-analysis of CEA (cut-off = 6.12ng/mL) and glucose (cut-off = 2.8mmol/L) added value to prediction.

S6 Artificial Intelligence for Automatic Diagnosis of Mucinous Pancreatic Cystic Lesions in Endoscopic Ultrasound: A Pilot Study

S6 Artificial Intelligence for Automatic Diagnosis of Mucinous Pancreatic Cystic Lesions in Endoscopic Ultrasound: A Pilot Study

Intracystic glucose is more accurate than carcinoembryonic antigen for the diagnosis of mucinous pancreatic cystic lesions: A meta-analysis

Intracystic glucose is more accurate than carcinoembryonic antigen for the diagnosis of mucinous pancreatic cystic lesions: A meta-analysis

ID: 3521525 DIAGNOSTIC ACCURACY OF INTRACYSTIC CONCENTRATIONS OF GLUCOSE AND CEA IN IDENTIFYING MUCIN PRODUCING CYSTIC NEOPLASMS OF THE PANCREAS: A MULTICENTER RETROSPECTIVE COHORT STUDY OF HISTOLOGICALLY-PROVEN CYSTS

Pancreatic cystic lesions (PCLs) are encountered incidentally in 2.4-19.6% of the general population and the mucinous subtypes carry malignant potential. Differentiating mucinous from non-mucinous PCLs can present a diagnostic challenge. Current guidelines recommend the use of cyst fluid carcinoembryonic antigen (CEA) to identify mucinous PCLs, however its sensitivity and specificity can vary widely. Through the needle biopsy or DNA mutational analysis have yielded favorable results but clinical applicability is limited by cost and inaccessibility. Intracystic glucose has shown promise in differentiating mucinous PCLs, but data are limited to frozen specimens and PCLs without uniform histologic diagnostic confirmation. The aim of this study was to assess the diagnostic accuracy of intracystic glucose in differentiating mucinous from non-mucinous PCLs from fresh fluid of PCLs with confirmatory histologic diagnoses.

Intracystic Glucose Levels Appear Useful for Diagnosis of Pancreatic Cystic Lesions: A Systematic Review and Meta-Analysis.

Carcinoembryonic antigen (CEA) in the pancreatic cystic fluid is the most important biomarker for differentiating mucinous from non-mucinous pancreatic cystic lesions (PCLs). However, recent studies have shown that glucose levels in pancreatic cystic fluid can discriminate mucinous from non-mucinous cysts. To perform a meta-analysis to determine the utility of intracystic fluid glucose of pancreatic mucinous cysts compared with intracystic CEA. We conducted a systematic review of the literature in the PubMed, OVID Medline, and Cochrane databases. This meta-analysis considers studies published up to October 2020. Six studies comprising 506 patients were selected; 61.2% of the population was female. Of the 480 PCLs, 287 (59.7%) were mucinous. Pooled sensitivity and specificity of cystic fluid glucose levels for mucinous PCLs were 91% and 85%, respectively. The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 6.33 and 0.11, respectively. Pooled diagnostic odds ratio (DOR) was 60.94. The pooled area under the summary receiver operating characteristic (SROC) curve was 0.959. Pooled sensitivity and specificity of pancreatic cystic fluid CEA levels were 61% and 93%. The PLR and NLR were 8.51 and 0.40, respectively. Pooled DOR was 23.52, and the pooled area under the SROC curve was 0.861. Glucose has become a useful method and appears to be better than CEA for differentiating between mucinous PCLs and non-mucinous PCLs. We suggest that the analysis of glucose in PCLs be routinely performed for the differential diagnosis of these lesions.

Mucin Expression in Mucinous Pancreatic Cysts: Can String Sign Test Predict Mucin Types? A Single Center Pilot Study.

Mucinous pancreatic cystic lesions (PCLs) express different mucin (MUC) types according to their histomorphologic types. High cystic fluid viscosity may help in the detection of mucinous PCLs. We hypothesized that high cystic fluid viscosity may be suggestive of a certain MUC type in mucinous PCLs. Prespecified MUC types (MUC1, MUC2, MUC4, MUC5AC, and MUC6) were evaluated in 18 definitively diagnosed mucinous PCLs with sufficient tissue material and prediagnostic cyst fluid viscosity evaluation-string sign (SS)-test. We evaluated the agreement of MUC expression with positive SS test results. Later, we compared cystic fluid carcinoembryonic antigen (CEA) between the prespecified MUC expressing and nonexpressing cyst types. A total of 18 mucinous PCL patients, 11 females, with mean age ± SD (59.7 ± 13.3) were included. Almost all malignant mucinous PCLs expressed MUC1 (71.4%) (P = .023). We found no significant agreement between the prespecified MUC types and positive SS, except MUC4 which had mild agreement. Also, no significant relation was found between cystic fluid CEA levels and MUC expression (P = .584). We did not detect a significantly moderate or good agreement between the prespecified MUC types and SS test. MUC1 was highly expressed in malignant mucinous cysts; however, it was incompatible with the SS test. MUC4 expression showed mild agreement with the SS test in a small number of patients.

Nonmucinous Cystic Lesions of the Pancreas.

Pancreatic cystic lesions are increasingly diagnosed. Among other criteria, they are often distinguished in mucinous versus nonmucinous cysts. Mucinous pancreatic cystic lesions have received increasing attention, especially those known as precursors of pancreatic ductal adenocarcinoma. However, the group of nonmucinous cystic lesions of the pancreas includes numerous entities that may pose a diagnostic challenge. Their accurate diagnosis and classification are crucial for adequate patient management. To review the spectrum of nonmucinous cystic lesions of the pancreas, taking into consideration their epidemiology and typical clinical context, their characteristic gross morphology and histomorphology, as well as their immunohistochemical and molecular profile. Literature was searched and reviewed with MEDLINE via PubMed. Macroscopic and microscopic images were obtained from the archives of the Institute of Pathology, Heinrich Heine University and University Hospital of Duesseldorf, Germany. Nonmucinous cysts of the pancreas comprise numerous, mostly rare entities displaying different biological behaviors. The most frequent are serous cystic neoplasms, solid-pseudopapillary neoplasms, cystic neuroendocrine tumors, and pancreatitis-associated pseudocysts. Accurate diagnosis can be achieved if characteristic clinical context, histomorphology, and immunoprofile are taken into account.

Cyst Fluid Carcinoembryonic Antigen Level Difference between Mucinous Cystic Neoplasms and Intraductal Papillary Mucinous Neoplasms.

Background/AimsThe role of cyst fluid carcinoembryonic antigen (CEA) level in differentiating mucinous pancreatic cystic lesions (PCLs) is controversial. We investigated the role of cyst fluid CEA in differentiating low-risk (LR)-intraductal papillary mucinous neoplasms (IPMNs) from high-risk (HR)-IPMNs and LR-mucinous cystic neoplasms (MCNs). MethodsThis was a retrospective study of 466 patients with PCLs who underwent endoscopic ultrasound-guided fine-needleaspiration over a 7-year period. On histology, low-grade dysplasia and intermediate-grade dysplasia were considered LR, whereas high-grade dysplasia and invasive carcinoma were considered HR. ResultsData on cyst fluid CEA levels were available for 50/102 mucinous PCLs with definitive diagnoses. The median CEA (range) levels were significantly higher in HR cysts than in LR cysts (2,624 [0.5–266,510] ng/mL vs. 100 [16.8–53,445]ng/mL, p=0.0012). The area under the receiver operating characteristic curve (AUROC) was 0.930 (95% confidence interval [CI], 0.5–0.8; p<0.001) for differentiating LR-IPMNs from LR-MCNs. The AUROC was 0.921 (95% CI, 0.823–1.000; p<0.001) for differentiating LR-IPMNs from HR-IPMNs. Both had a CEA cutoff level of >100ng/mL, with a negative predictive value (NPV) of 100%. ConclusionsCyst fluid CEA levels significantly vary between LR-IPMNs, LR-MCNs, and HR-IPMNs. A CEA cutoff level of >100ng/mL had a 100% NPV in differentiating LR-IPMNs from LR-MCNs and HR-IPMNs.