Majumder, Shounak

ImportanceIncreased cancer risk in first-degree relatives of probands with pancreatic ductal adenocarcinoma (PDAC probands) who carry pathogenic or likely pathogenic germline variants (PGVs) in cancer syndrome–associated genes encourages cascade genetic testing. To date, unbiased risk estimates for the development of cancers on a gene-specific basis have not been assessed.ObjectiveTo quantify the risk of development of PDAC and extra-PDAC among first-degree relatives of PDAC probands who carry a PGV in 1 of 9 cancer syndrome–associated genes—ATM, BRCA1, BRCA2, PALB2, MLH1, MSH2, MSH6, PMS2, and CDKN2A.Design, Setting, and ParticipantsThis case series focused on first-degree relatives of PDAC probands carrying PGVs in specific cancer syndrome–associated genes. The cohort comprised clinic-ascertained patients enrolled in the Mayo Clinic Biospecimen Resource for Pancreas Research registry with germline genetic testing. In total, 234 PDAC probands carrying PGVs were drawn from the prospective research registry of 4562 participants who had undergone genetic testing of cancer syndrome–associated genes. Demographic and cancer-related family histories were obtained by questionnaire. The data were collected from October 1, 2000, to December 31, 2021.Main Outcomes and MeasuresFor the PDAC probands, the genetic test results of the presence of PGVs in 9 cancer syndrome–associated genes were obtained by clinical testing. Cancers (ovary, breast, uterus or endometrial, colon, malignant melanoma, and pancreas) among first-degree relatives were reported by the probands. Standardized incidence ratios (SIRs) were used to estimate cancer risks among first-degree relatives of PDAC probands carrying a PGV.ResultsIn total, 1670 first-degree relatives (mean [SD] age, 58.1 [17.8] years; 853 male [51.1%]) of 234 PDAC probands (mean [SD] age, 62.5 [10.1] years; 124 male [53.0%]; 219 [94.4%] White; 225 [98.7%] non-Hispanic or non-Latino]) were included in the study. There was a significantly increased risk of ovarian cancer in female first-degree relatives of probands who had variants in BRCA1 (SIR, 9.49; 95% CI, 3.06-22.14) and BRCA2 (SIR, 3.72; 95% CI, 1.36-8.11). Breast cancer risks were higher with BRCA2 variants (SIR, 2.62; 95% CI, 1.89-3.54). The risks of uterine or endometrial cancer (SIR, 6.53; 95% CI, 2.81-12.86) and colon cancer (SIR, 5.83; 95% CI, 3.70-8.75) were increased in first-degree relatives of probands who carried Lynch syndrome mismatch repair variants. Risk of PDAC was also increased for variants in ATM (SIR, 4.53; 95% CI, 2.69-7.16), BRCA2 (SIR, 3.45; 95% CI, 1.72-6.17), CDKN2A (SIR, 7.38; 95% CI, 3.18-14.54), and PALB2 (SIR, 5.39; 95% CI, 1.45-13.79). Melanoma risk was elevated for first-degree relatives of probands with CDKN2A variants (SIR, 7.47; 95% CI, 3.97-12.77).Conclusions and RelevanceIn this case series, the presence of PGVs in 9 cancer syndrome–associated genes in PDAC probands was found to be associated with increased risk of 6 types of cancers in first-degree relatives. These gene-specific PDAC and extra-PDAC cancer risks may provide justification for clinicians to counsel first-degree relatives about the relevance and importance of genetic cascade testing, with the goal of higher uptake of testing.

Purpose This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)–enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. Methods Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected (“corrected CNN [c-CNN]” segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. Results Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation–derived volumes [76.6 cm3 (20.2 cm3), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%–87% [P < 0.0001]). Conclusions Convolutional neural network–enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

INTRODUCTION: Observational studies have suggested an increased risk of pancreatic ductal adenocarcinoma (PDAC) in patients with acute and chronic pancreatitis. We conducted a systematic review and meta-analysis to evaluate the magnitude of this association and summarize the published epidemiological evidence. METHODS: We searched electronic databases (MEDLINE, Embase, Web of Science, Cochrane, and Scopus) and reference lists until January 18, 2021. Studies reporting quantitative association between pancreatitis and PDAC were included and assessed for eligibility, data abstraction, and risk of bias. Standardized incidence ratios (SIRs) were pooled using the random-effects model. RESULTS: Twenty-five cohort and case-control studies met inclusion criteria. Meta-analysis of 12 chronic pancreatitis (CP) studies demonstrated an increased risk of PDAC in patients with CP (SIR: 22.61, 95% confidence interval [CI]: 14.42–35.44). This elevated risk persisted in subgroup analysis of studies that excluded patients diagnosed with PDAC within 2 years of CP diagnosis (SIR: 21.77, 95% CI: 14.43–32.720). The risk was higher in hereditary pancreatitis (SIR: 63.36, 95% CI: 45.39–88.46). The cumulative incidence rates of PDAC in CP increased with follow-up duration. Limited evidence in acute pancreatitis indicates higher PDAC risk in the subset of patients eventually diagnosed with CP. PDAC seems to be uncommon in patients with autoimmune pancreatitis, with 8 reported cases in 358 patients with autoimmune pancreatitis across 4 studies. DISCUSSION: There is an increased risk of PDAC in patients with CP, and incidence rates increase with CP disease duration. Our results indicate that PDAC surveillance may be considered in individuals with long-standing CP.

Purpose of review Pancreatic ductal adenocarcinoma (PDAC) is third leading cause of cancer death in the United States, a lethal disease with no screening strategy. Although diagnosis at an early stage is associated with improved survival, clinical detection of PDAC is typically at an advanced symptomatic stage when best in class therapies have limited impact on survival. Recent findings In recent years this status quo has been challenged by the identification of novel risk factors, molecular markers of early-stage disease and innovations in pancreatic imaging. There is now expert consensus that screening may be pursued in a cohort of individuals with increased likelihood of developing PDAC based on genetic and familial risk. Summary The current review summarizes the known risk factors of PDAC, current knowledge and recent observations pertinent to early detection of PDAC in these risk groups and outlines future approaches that will potentially advance the field.

Abstract Purpose: We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case–control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC. Experimental Design: Thirteen MDMs (GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9, and NTRK3) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated. Results: The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89–0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87–0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98–1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76–0.92), CA19-9 alone was 0.87 (0.79–0.94), and combined MDM-CA19-9 panel was 0.90 (0.84–0.97) significantly better compared with either MDMs alone or CA19-9 alone (P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%–99%) for stage I disease and 82% (68%–92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86–0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84–0.93) versus 0.97 (0.94–0.99) for the combined MDM-CA19-9 panel (P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%–98%), with an observed specificity of 92% at the preset specificity of 97.5%. Conclusions: Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

Objective The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. Design A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. Results From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). Conclusion The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.

Abstract Background Endoscopic intervention for pancreatic fluid collections (PFCs) with disconnected pancreatic duct syndrome (DPDS) has been associated with failures and increased need for additional endoscopic and non-endoscopic interventions. The primary aim of this study was to determine the outcomes of endoscopic ultrasound (EUS)-guided transmural drainage of PFCs in patients with DPDS. Methods In patients undergoing EUS-guided drainage of PFCs from January 2013 to January 2018, demographic profiles, procedural indications and details, adverse events, outcomes, and subsequent interventions were retrospectively collected. Overall treatment success was determined by PFC resolution on follow-up imaging or stent removal without recurrence. Results EUS-guided drainage of PFCs was performed in 141 patients. DPDS was present in 57 of them (40 %) and walled-off necrosis was the most frequent type of PFC (55 %). DPDS was not associated with lower clinical success, increased number of repeat interventions, or increased time to PFC resolution. Patients with DPDS were more likely to be treated with permanent transmural plastic double-pigtail stents (odds ratio [OR] 6.4; 95 % confidence interval [CI] 2.5 – 16.5; P < 0.001). However, when stents were removed, DPDS was associated with increased PFC recurrence after stent removal (OR 8.0; 95 %CI 1.2 – 381.8; P = 0.04). Conclusions DPDS frequently occurs in patients with PFCs but does not negatively impact successful resolution. DPDS is associated with increased PFC recurrence after stent removal.

e16705 Background: The prevalence of pancreatic neuroendocrine tumors (PNETs) has increased in the last decade. Despite being clinically asymptomatic PNETs can be biologically aggressive. There is currently no reliable non-invasive diagnostic biomarker for PNETs. In this study we aimed to identify and validate methylated DNA marker (MDM) candidates that differentiate PNET from normal pancreas. Methods: For discovery, reduced representation bisulfite sequencing (RRBS) was performed on DNA extracted from frozen normal pancreas (n = 13) and PNET (n = 51) tissues. Area under the receiver operator characteristic curve (AUC), fold-change, and p-value criteria selected candidates MDMs for blinded validation in independent FFPE tissues from primary PNET (n = 67; solid = 50, cystic = 17), normal pancreas controls (n = 24), and normal buffy coat (n = 36) using methylation specific PCR. MDM distributions in primary PNETs were compared to primary lung (n = 36) and small bowel (n = 36) NETs and metastatic PNET tissue (n = 25). The discrimination accuracy of candidate markers was summarized as the AUC with corresponding 95% confidence intervals (CI). Results: From the RRBS discovery, 31 candidate MDMs were selected for validation. Four MDMs ( SRRM3, HCN2, SPTBN4 and TMC6) achieved individual AUCs ≥0.95 in the validation set (Table). These MDMs were similarly discriminant in metastatic PNET tissue and in primary lung and small bowel NETs. Three out of these 4 MDMs perfectly differentiated PNET tissue from buffy coat with AUC of 1 and may be ideally suited for further development of a blood-based assay. Conclusions: We identified and validated novel MDMs in tissue that discriminate PNETs from controls with normal pancreas and buffy coat with high accuracy. These MDMs also differentiated metastatic PNETs from normal pancreas tissue. Further exploration of these candidate tissue MDMs in plasma can potentially guide diagnosis and management of PNETs. Funding: P30DK084567. [Table: see text]