Faculty Bibliography

Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screening in at-risk populations and detection of early cancer has the potential to significantly alter survival. In this study, we aim to develop a predictive model to identify patients at risk for developing new-onset PC at two and a half to three year time frame. We used the Electronic Health Records (EHR) of a large medical system from 2000 to 2021 (N = 537,410). The EHR data analyzed in this work consists of patients' demographic information, diagnosis records, and lab values, which are used to identify patients who were diagnosed with pancreatic cancer and the risk factors used in the machine learning algorithm for prediction. We identified 73 risk factors of pancreatic cancer with the Phenome-wide Association Study (PheWAS) on a matched case-control cohort. Based on them, we built a large-scale machine learning algorithm based on EHR. A temporally stratified validation based on patients not included in any stage of the training of the model was performed. This model showed an AUROC at 0.742 [0.727, 0.757] which was similar in both the general population and in a subset of the population who has had prior cross-sectional imaging. The rate of diagnosis of pancreatic cancer in those in the top 1 percentile of the risk score was 6 folds higher than the general population. Our model leverages data extracted from a 6-month window of time in the electronic health record to identify patients at nearly sixfold higher than baseline risk of developing pancreatic cancer 2.5-3 years from evaluation. This approach offers an opportunity to define an enriched population entirely based on static data, where current screening may be recommended.

BACKGROUND AND AIMS/OBJECTIVE:Fully covered self-expandable metal stents (FCSEMSs) are widely used in benign upper gastrointestinal (GI) conditions, but stent migration remains a limitation. An over-the-scope clip (OTSC) device (Ovesco Endoscopy) for stent anchoring has been recently developed. The aim of this study was to evaluate the effect of OTSC fixation on SEMS migration rate. METHODS:A retrospective review of consecutive patients who underwent FCSEMS placement for benign upper GI conditions between 1/2011 and 10/2022 at 16 centers. The primary outcome was rate of stent migration. The secondary outcomes were clinical success and adverse events. RESULTS:A total of 311 (no fixation 122, OTSC 94, endoscopic suturing 95) patients underwent 316 stenting procedures. Compared to the no fixation (NF) group (n=49, 39%), the rate of stent migration was significantly lower in the OTSC (SF) (n=16, 17%, p=0.001) and endoscopic suturing (ES) group (n=23, 24%, p=0.01). The rate of stent migration was not different between the SF and ES groups (p=0.2). On multivariate analysis, SF (OR 0.34, CI 0.17-0.70, p<0.01) and ES (OR 0.46, CI 0.23-0.91, p=0.02) were independently associated with decreased risk of stent migration. Compared to the NF group (n=64, 52%), there was a higher rate of clinical success in the SF (n=64, 68%; p=0.03) and ES group (n=66, 69%; p = 0.02). There was no significant difference in the rate of adverse events between the three groups. CONCLUSION/CONCLUSIONS:Stent fixation using OTSC is safe and effective at preventing stent migration and may also result in improved clinical response.

BACKGROUND:Pancreatic ductal adenocarcinoma (PDAC) presents a redoubtable challenge due to late-stage diagnosis and limited treatment options, necessitating innovative therapeutic strategies. METHODS:Here, we report our results investigating the safety and efficacy of talimogene laherparepvec (T-VEC), an FDA-approved oncolytic herpes simplex virus type 1, in patients with advanced PDAC. Nine patients with treatment-refractory advanced PDAC received escalating doses of T-VEC via endoscopic injection. RESULTS:While no objective responses were observed, stable disease was achieved in 44% of patients, with a median overall survival of 7.8 months, including one patient who survived 28 months. Adverse events were manageable, with the maximum tolerated dose determined to be 108 PFU/mL. CONCLUSION/CONCLUSIONS:Our findings underscore the feasibility of intratumoral T-VEC administration in advanced PDAC. Further investigation, particularly in combination with immunotherapies administered systemically is warranted to more optimally assess T-VEC in this challenging malignancy.ClinicalTrials.gov Identifier: NCT03086642.

BACKGROUND AND STUDY AIMS/UNASSIGNED:EUS-guided gallbladder drainage (EUS-GBD) using lumen apposing metal stents (LAMS) has excellent technical and short-term clinical success for acute cholecystitis (AC). The goals of this study were to determine the long-term clinical outcomes and adverse events (AEs) of EUS-GBD with LAMS. PATIENTS AND METHODS/UNASSIGNED:A multicenter, retrospective study was conducted at 18 US tertiary care institutions. Inclusion criteria: any AC patient with attempted EUS-GBD with LAMS and minimum 30-day post-procedure follow-up. Long-term clinical success was defined as absence of recurrent acute cholecystitis (RAC) > 30 days and long-term AE was defined as occurring > 30 days from the index procedure. RESULTS/UNASSIGNED:<0.01) were associated with RAC. AEs occurred in 38 of 109 patients (34.9%) at any time, and in 10 of 109 (9.17%) > 30 days from the index procedure. Most long-term AEs (7 of 109; 6.42%) were LAMS-specific. No technical or clinical factors were associated with occurrence of AEs. LAMS were removed in 24 of 109 patients (22%). There was no difference in RAC or AEs whether LAMS was removed or not. CONCLUSIONS/UNASSIGNED:EUS-GBD with LAMS has a high rate of long-term clinical success and modest AE rates in patients with AC and is a reasonable destination therapy for high-risk surgical candidates.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We introduced a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (±7.2%, at case level) with CT, 85.0% (±7.9%) with T1 W MRI, and 86.3% (±6.4%) with T2 W MRI. There was a high correlation for pancreas volume prediction with R² of 0.91, 0.84, and 0.85 for CT, T1 W, and T2 W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1 W and T2 W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet.

INTRODUCTION/BACKGROUND:Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). Although EUS is preferred for pancreatic lesion evaluation, its diagnostic accuracy is suboptimal. This multicentric study aims to develop a convolutional neural network (CNN) for detecting and distinguishing PCN (namely M-PCN and NM-PCN) and PSL (particularly P-DAC and P-NET). METHODS:A CNN was developed with 378 EUS examinations from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo). About 126.000 images were obtained-19.528 M-PCN, 8.175 NM-PCN, 64.286 P-DAC, 29.153 P-NET, and 4.858 normal pancreas images. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-DAC from P-NET. The total data set was divided into a training and testing data set (used for model's evaluation) in a 90/10% ratio. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, and accuracy. RESULTS:The CNN had 99.1% accuracy for identifying normal pancreatic tissue, 99.0% and 99.8% for M-PCN and NM-PCN, respectively. P-DAC and P-NET were distinguished with 94.0% accuracy. DISCUSSION/CONCLUSIONS:Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using examinations from 4 centers in 2 continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation.

OBJECTIVES/OBJECTIVE:We sought to determine the yield of somatic mutational analysis from EUS-guided biopsies of pancreatic adenocarcinoma compared to that of surgical resection and to assess the impact of these results on oncologic treatment. METHODS:We determined the yield of EUS sampling and surgical resection. We evaluated the potential impact of mutational analysis by identifying actionable mutations and its direct impact by reviewing actual treatment decisions. RESULTS:Yield of EUS sampling was 89.5%, comparable to the 95.8% yield of surgical resection. Over a quarter in the EUS cohort carried actionable mutations, and of these, over one in six had treatment impacted by mutational analysis. CONCLUSIONS:EUS sampling is nearly always adequate for somatic testing and may have substantial potential and real impact on treatment decisions.