BACKGROUND: Early diagnosis of pancreatic ductal adenocarcinoma (PDAC) has been a longstanding challenge. The prognosis of patients with PDAC depends on the stage at diagnosis. It is necessary to identify biomarkers for the detection and differentiation of pancreatic tumors and optimize PDAC sample preparation procedures for DNA and RNA analysis. Most molecular studies are done using paraffin-embedded blocks; however, the integrity of DNA and RNA is often compromised in this format. Moreover, RNA isolated from human pancreatic tissue samples is generally of low quality, in part, because of the high concentration of endogenous pancreatic RNAse activity present.
OBJECTIVE: To assess the potential of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) to obtain specimens from pancreatic neoplasms for subsequent RNA molecular profiling, including next-generation sequencing (NGS).
METHODS: Thirty-four EUS-FNA samples were included in this study: PDAC (n = 15), chronic pancreatitis (n = 5), pancreatic cysts (n = 14), mucinous cysts (mucinous cystic neoplasia/intraductal papillary mucinous neoplasia) n = 7, serous cystic neoplasms n = 5, and pseudocysts n = 2. Cyst material consisted of cyst fluid and cyst wall samples obtained by through-the-needle biopsy (TTNB). Samples were stored at -80 °C until analysis. RNA purity (A260/230, A260/280 ratios), concentration, and integrity (RIN) were assessed. Real-time polymerase chain reaction was conducted on all samples, and small RNA libraries were prepared from solid mass samples.
RESULTS: RNA was successfully extracted from 29/34 (85%) EUS-FNA samples: 100% pancreatic adenocarcinoma samples, 100% chronic pancreatitis samples, 70% pancreatic fluid cyst samples, and 50% TTNB samples. The relative expression of GAPDH and HPRT were obtained for all successfully extracted RNA samples (n = 29) including low-quality RNA specimens. Low concentration and nonoptimal RIN values (no less than 3) of RNA extracted from EUS-FNA samples did not prevent NGS library preparation. The suitability of cyst fluid samples for RNA profiling varied. The quality of RNA extracted from mucinous cyst fluid had a median RIN of 7.7 (5.0-8.2), which was compatible with that from solid neoplasms [6.2 (0-7.8)], whereas the quality of the RNA extracted from all fluids of serous cystic neoplasms and TTNB samples had a RIN of 0.
CONCLUSIONS: The results demonstrate the high potential of EUS-FNA material for RNA profiling of various pancreatic lesions, including low-quality RNA specimens.

BACKGROUND: Pancreatic cancer remains one of the deadliest cancers in the United States. Some types of pancreatic cysts, which are being detected more frequently and often incidentally on imaging, have the potential to develop into pancreatic cancer and thus provide a valuable window of opportunity for cancer interception. Although racial disparity in pancreatic cancer has been described, little is known regarding health disparities in pancreatic cancer prevention. In the present study, we investigate potential health disparities along the continuum of care for pancreatic cancer.
METHODS: The racial and ethnic composition of pancreatic patients at high-volume centers in Indiana were evaluated, representing patients undergoing surgery for pancreatic cancer (n=390), participating in biobanking (972 pancreatic cancer patients and 1984 patients with pancreatic disease), or being monitored for pancreatic cysts at an early detection center (n=1514). To assess racial disparities and potential differences in decision-making related to pancreatic cancer prevention and early detection, an exploratory online survey was administered through a volunteer registry (n=708).  Results: We show that despite comprising close to 10% or 30% of the Indiana or Indianapolis population, respectively, African Americans make up only about 4-5% of our study cohorts consisting of patients undergoing pancreatic surgery or participating in biobanking and early detection. Analysis of online survey results revealed that given the hypothetical situation of being diagnosed with a pancreatic cyst or pancreatic cancer, the vast majority of respondents (>90%) would agree to undergo surveillance or surgery, respectively, regardless of race. Only a minority (3-12%) acknowledged any significant transportation, financial, or emotional barriers that would impact a decision to undergo surveillance or surgery. This suggests that the observed racial disparities may be due in part to the existence of other barriers that lie upstream of this decision point.
CONCLUSIONS: Racial disparities exist not only for pancreatic cancer but also at earlier points along the continuum of care such as prevention and early detection. To our knowledge, this is the first study to document racial disparity in the management of patients with pancreatic cysts who are at risk of developing pancreatic cancer. Our results suggest that improving access to information and care for such at-risk individuals may lead to more equitable outcomes.

BACKGROUND: Pancreatic cysts are often incidentally detected on routine imaging studies. Of these, mucinous cysts have a malignant potential. Several guidelines propose different management strategies, and implementation in patient care is inconsistent in the absence of dedicated infrastructure.
METHODS: To address the challenges of pancreatic cyst diagnosis and management, we established a multidisciplinary pancreas cyst clinic (PCC) within our health system. This clinic encompasses both tertiary care academic centers and community hospitals, with leadership from surgical oncology, gastroenterology, and radiology. Our PCC\'s primary goal is to provide accurate diagnosis and tailored management recommendations for all patients with pancreatic cysts. Additionally, we maintain a prospective database to study the disease\'s natural history and the outcomes of various treatment strategies.
UNASSIGNED: The clinic meets once per week for 45 min virtually via Zoom in the mornings. Patients are referred via electronic medical record (EMR) order, telephone call, or email from patient or referring provider. A dedicated advanced practice provider reviews referrals several times per day, calls patients to gather clinical data, ensures imaging is uploaded, and coordinates logistical aspects of the meeting during the dedicated time. Conferences are attended by representatives from surgery, radiology, medical pancreatology, and interventional gastroenterology. Each patient case is reviewed in detail and recommendations are submitted to referring providers and patients via an EMR message and letter. For patients requiring imaging surveillance, patients are followed longitudinally by the referring provider, gastroenterology team, or surgical team. For patients requiring endoscopic ultrasound (EUS) or surgical consultation, expedited referral to these services is made with prompt subsequent evaluation.
RESULTS: A total of 1052 patients from our health system were evaluated between 2020 and 2021. Of these, 196 (18.6 %) underwent EUS, 41 (3.9 %) underwent upfront surgical resection, and the remainder were referred to gastroenterology (141-13.4 %), surgery (314-29.8 %), or back to their referring provider (597-56.7 %) for ongoing surveillance in collaboration with their primary care provider (PCP). Of cysts under surveillance, 61.3 % remained stable, 13.2 % increased in size, and 2 % decreased in size. A total of 2.3 % of patients were recommended to discontinue surveillance.
CONCLUSIONS: The PCC provides infrastructure that has served to provide multidisciplinary review and consensus recommendations to patients with pancreatic cysts. This has served to improve the application of guidelines while providing individualized recommendations to each patient, while aiding non-expert referring providers throughout the region.

BACKGROUND: Pancreatic cysts are frequently observed in patients with von Hippel-Lindau disease (VHL), and they are considered clinically not important. This study aimed to evaluate the association between pancreatic cysts and diabetes mellitus (DM) in patients with VHL.
METHODS: Among patients who were on a patient list at the VHL Center at Kyoto University Hospital as of December 2022, those who had undergone an upper abdominal magnetic resonance imaging study after 2010 were retrospectively evaluated. The presence or absence of DM and high glycated hemoglobin (HbA1c) levels (>6.0%) were assessed. Patients were divided into two groups: those with DM or high HbA1c levels, and those without DM or high HbA1c levels. The area of the whole pancreas, including the pancreatic cysts and tumors, the area of the pancreatic cysts, and the percentage of pancreatic cysts, calculated by dividing the area of pancreatic cysts by the area of the whole pancreas, were measured on T2-weighted magnetic resonance images and compared between the two groups.
RESULTS: Thirty-six patients with VHL, comprising 22 men and 14 women, with a mean age of 36.4 years (range, 11-79 years), were identified. Seven patients had DM, and two additional patients had high HbA1c levels. The area of the pancreatic cysts (p = 0.0013) was significantly larger and the percentage of the pancreatic cysts (p = 0.0016) was significantly higher in patients with DM or high HbA1c levels (n = 9) than in patients without DM or high HbA1c levels (n = 27); however, the difference in the area of the whole pancreas was not significant (p = 0.068).
CONCLUSIONS: Our findings suggest that patients with VHL who have a large area covered by pancreatic cysts are more likely to have DM than those without.

OBJECTIVE: Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection.
METHODS: Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package.
RESULTS: Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5-160 mm), with a mean follow-up of 72 months (range 3-266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals.
CONCLUSIONS: SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.

暂无摘要。

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.

This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association\'s ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.

Incidental pancreatic cysts are highly prevalent, with management dependent on the risk of malignant progression. Serous cystadenomas (SCAs) are the most common benign pancreatic cysts seen on imaging. They have typical morphological patterns but may also show atypical features that mimic precancerous and cancerous cysts. If a confident diagnosis of SCA is made, no further follow-up is warranted. Therefore, a preoperative distinction between SCA and precancerous or cancerous lesions is critically essential. Distinguishing an SCA from other types of pancreatic cysts on imaging remains a challenge, thus leading to misdiagnosis and ramifications. This review summarizes the current evidence on diagnosing and managing SCA.

Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65-75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.