Molecular analysis of pancreatic cystic neoplasm in routine clinical practice

Glucose and lactate levels are lower in EUS-aspirated cyst fluid of mucinous vs non-mucinous pancreatic cystic lesions

BACKGROUND

Distinguishing mucinous (M) pancreatic cystic neoplasms (PCNs) from non-mucinous (NM) is challenging but crucial. Low intracystic glucose level has shown diagnostic tool promise, however further investigation is needed to understand metabolic processes.

AIMS

To compare the diagnostic accuracy of intracystic glucose and CEA levels in a large cohort and explore lactate levels as potential marker.

METHODS

PCNs≥15 mm which underwent EUS-fine needle aspiration were prospectively enrolled. Glucose, CEA and lactate levels were measured. Diagnostic accuracy for M-PCN diagnosis was evaluated using surgical/cytology reports or multidisciplinary evaluations.

RESULTS

169 PCNs were included (64 % M-PCNs). Median intracystic glucose was significantly lower in M-PCNs (1 mg/dL) compared to NM-PCNs (101 mg/dL); mean intracystic CEA was significantly higher in M-PCNs (152.5 ng/mL) compared to NM-PCNs (0.3 ng/mL). ROC curve analysis revealed best glucose cut-off ≤58 mg/dL (accuracy 93.5 %) and CEA cut-off >2.5 ng/mL (accuracy 90.5 %) for M-PCNs. Intracystic lactates were significantly lower in M-PCNs correlating directly with glucose. Single glucose dosage evidenced best diagnostic accuracy respect markers combination.

CONCLUSION

Intracystic glucose demonstrated high diagnostic utility for M-PCNs differentiation, surpassing CEA. Lactate levels correlated with glucose, suggesting their uptake by M-PCNs cells. These findings contribute to a better metabolic landscape understanding glucose use as diagnostic marker.

Endoscopic Ultrasound-Guided Ablation of Premalignant Pancreatic Cysts and Pancreatic Cancer

Pancreatic cancer is on the rise and expected to become the second leading cause of cancer-related death by 2030. Up to a one-fifth of pancreatic cancers may arise from mucinous pancreatic cysts, which are frequently present in the general population. Currently, surgical resection is the only curative approach for pancreatic cancer and its cystic precursors. However, only a dismal proportion of patients are eligible for surgery. Therefore, novel treatment approaches to treat pancreatic cancer and precancerous pancreatic cysts are needed. Endoscopic ultrasound (EUS)-guided ablation is an emerging minimally invasive method to treat pancreatic cancer and premalignant pancreatic cysts. Different ablative modalities have been used including alcohol, chemotherapy agents, and radiofrequency ablation. Cumulative data over the past two decades have shown that endoscopic ablation of mucinous pancreatic cysts can lead to cyst resolution in a significant proportion of the treated cysts. Furthermore, novel data are emerging about the ability to endoscopically ablate early and locally advanced pancreatic cancer. In this review, we aim to summarize the available data on the efficacy and safety of the different EUS-ablation modalities for the management of premalignant pancreatic cysts and pancreatic cancer.

Advances in the Diagnosis of Pancreatic Cystic Lesions

Pancreatic cystic lesions (PCLs) are a heterogenous group of lesions ranging from benign to malignant. There has been an increase in PCLs prevalence in recent years, mostly due to advances in imaging techniques, increased awareness of their existence and population aging. Reliable discrimination between neoplastic and non-neoplastic cystic lesions is paramount to ensuring adequate treatment and follow-up. Although conventional diagnostic techniques such as ultrasound (US), magnetic resonance imaging (MRI) and computer tomography (CT) can easily identify these lesions, assessing the risk of malignancy is limited. Endoscopic ultrasound (EUS) is superior to cross-sectional imaging in identifying potentially malignant lesions due to its high resolution and better imaging characteristics, and the advantage of allowing for cyst fluid sampling via fine-needle aspiration (FNA). More complex testing, such as cytological and histopathological analysis and biochemical and molecular testing of the aspirated fluid, can ensure an accurate diagnosis.

Evaluating Pancreatic and Biliary Neoplasms with Small Biopsy-Based Next Generation Sequencing (NGS): Doing More with Less

Simple Summary

Pancreatic cancer and cholangiocarcinoma are aggressive diseases mostly diagnosed at an advanced and inoperable stage. This review presents the value of next-generation sequencing (NGS) when performed on small biopsies—including fine-needle aspiration/biopsy samples, brushings, pancreatic juice and bile, and also blood—in the field of pancreatobiliary neoplasia. NGS could guide physicians while evaluating pancreatic solid and cystic lesions or suspicious biliary strictures, performing surveillance in high-risk individuals, or monitoring the disease and assessing prognosis in already diagnosed cancer patients. Evidence suggests that NGS performed on small biopsies is a robust tool for the diagnosis and pre-operative risk stratification of pancreatic and biliary lesions, whereas it also carries significant prognostic and therapeutic value. However, effective standardization of the pre-analytical and analytical assay parameters used for each clinical scenario is needed to fully implement NGS into routine practice and provide more personalized management in patients with suspected or established pancreatobiliary neoplasia.

Abstract

Pancreatic cancer and cholangiocarcinoma are lethal diseases mainly diagnosed at an inoperable stage. As pancreatobiliary surgical specimens are often unavailable for further molecular testing, this review aimed to highlight the diagnostic, prognostic, and therapeutic impact of next-generation sequencing (NGS) performed on distinct small biopsies, including endoscopic ultrasound fine-needle aspirations and biopsies of pancreatic solid and cystic lesions, biliary duct brushings, and also “liquid biopsies” such as the pancreatic juice, bile, and blood. NGS could clarify indeterminate pancreatic lesions or biliary strictures, for instance by identifying TP53 or SMAD4 mutations indicating high-grade dysplasia or cancer. It could also stratify pancreatic cystic lesions, by distinguishing mucinous from non-mucinous cysts and identifying high-risk cysts that should be excised in surgically fit patients, whereas the combination of cytology, elevated cystic CEA levels and NGS could improve the overall diagnostic accuracy. When NGS is performed on the pancreatic juice, it could stratify high-risk patients under surveillance. On the plasma, it could dynamically monitor the disease course and response to therapy. Notably, the circulating tumor DNA (ctDNA) levels have been associated with staging, grading, and survival. Lastly, NGS has shown potential in identifying potentially actionable molecular alterations. In conclusion, NGS applied on small biopsies could carry significant diagnostic, prognostic, and therapeutic value.

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

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.