Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Systematic review on surveillance for non-resected branch-duct intraductal papillary mucinous neoplasms of the pancreas.

BACKGROUND: The management of branch-duct type intraductal papillary mucinous neoplasms (BD-IPMN) varies in existing guidelines. This study investigated the optimal surveillance protocol and safe discontinuation of surveillance considering natural history in non-resected IPMN, by systematically reviewing the published literature. METHODS: This review was guided by PRISMA. Research questions were framed in PICO format "CQ1-1: Is size criteria helpful to determine surveillance period? CQ1-2: How often should surveillance be carried out? CQ1-3: When should surveillance be discontinued? CQ1-4: Is nomogram predicting malignancy useful during surveillance?". PubMed was searched from January-April 2022. RESULTS: The search generated 2373 citations. After screening, 83 articles were included. Among them, 33 studies were identified for CQ1-1, 19 for CQ1-2, 26 for CQ1-3 and 12 for CQ1-4. Cysts <1.5 or 2 cm without worrisome features (WF) were described as more indolent, and most studies advised an initial period of surveillance. The median growth rate of cysts <2 cm ranged from 0.23 to 0.6 mm/year. Patients with cysts <2 cm showing no morphological changes and no WF after 5-years of surveillance have minimal malignancy risk of 0-2%. Two nomograms created with over 1000 patients had AUCs of around 0.8 and appear to be feasible in a real-world practice. CONCLUSIONS: For patients with suspected BD-IPMN <2 cm and no other WF, less frequent surveillance is recommended. Surveillance may be discontinued for cysts that remain stable during 5-year surveillance, with consideration of patient condition and life expectancy. With this updated surveillance strategy, patients with non-worrisome BD-IPMN should expect more streamlined management and decreased healthcare utilization.

Long-Term Outcomes and Risk of Pancreatic Cancer in Intraductal Papillary Mucinous Neoplasms.

Importance: Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cysts that can give rise to pancreatic cancer (PC). Limited population data exist on their prevalence, natural history, or risk of malignant transformation (IPMN-PC). Objective: To fill knowledge gaps in epidemiology of IPMNs and associated PC risk by estimating population prevalence of IPMNs, associated PC risk, and proportion of IPMN-PC. Design, Setting, and Participants: : This retrospective cohort study was conducted in Olmsted County, Minnesota. Using the Rochester Epidemiology Project (REP), patients aged 50 years and older with abdominal computed tomography (CT) scans between 2000 and 2015 were randomly selected (CT cohort). All patients from the REP with PC between 2000 and 2019 were also selected (PC cohort). Data were analyzed from November 2021 through August 2023. Main outcomes and Measures: CIs for PC incidence estimates were calculated using exact methods with the Poisson distribution. Cox models were used to estimate age, sex, and stage-adjusted hazard ratios for time-to-event end points. Results: The CT cohort included 2114 patients (1140 females [53.9%]; mean [SD] age, 68.6 [12.1] years). IPMNs were identified in 231 patients (10.9%; 95% CI, 9.7%-12.3%), most of which were branch duct (210 branch-duct [90.9%], 16 main-duct [6.9%], and 5 mixed [2.2%] IPMNs). There were 5 Fukuoka high-risk (F-HR) IPMNs (2.2%), 39 worrisome (F-W) IPMNs (16.9%), and 187 negative (F-N) IPMNs (81.0%). After a median (IQR) follow-up of 12.0 (8.1-15.3) years, 4 patients developed PC (2 patients in F-HR and 2 patients in F-N groups). The PC incidence rate per 100 person years for F-HR IPMNs was 34.06 incidents (95% CI, 4.12-123.02 incidents) and not significantly different for patients with F-N IPMNs compared with patients without IPMNs (0.16 patients; 95% CI, 0.02-0.57 patients vs 0.11 patients; 95% CI, 0.06-0.17 patients; P = .62). The PC cohort included 320 patients (155 females [48.4%]; mean [SD] age, 72.0 [12.3] years), and 9.8% (95% CI, 7.0%-13.7%) had IPMN-PC. Compared with 284 patients with non-IPMN PC, 31 patients with IPMN-PC were older (mean [SD] age, 76.9 [9.2] vs 71.3 [12.5] years; P = .02) and more likely to undergo surgical resection (14 patients [45.2%] vs 60 patients [21.1%]; P = .003) and more-frequently had nonmetastatic PC at diagnosis (20 patients [64.5%] vs 130 patients [46.8%]; P = .047). Patients with IPMN-PC had better survival (adjusted hazard ratio, 0.62; 95% CI, 0.40-0.94; P = .03) than patients with non-IPMN PC. Conclusions and Relevance: In this study, CTs identified IPMNs in approximately 10% of patients aged 50 years or older. PC risk in patients with F-N IPMNs was low and not different compared with patients without IPMNs; approximately 10% of patients with PC had IPMN-PC, and they had better survival compared with patients with non-IPMN PC.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

Utility of Urgent Endoscopic Retrograde Cholangiopancreatography in Patients with Predicted Mild Acute Pancreatitis and Cholestasis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) within 72 h is suggested for patients presenting with acute biliary pancreatitis (ABP) and biliary obstruction without cholangitis. This study aimed to identify if urgent ERCP (within 24 h) improved outcomes compared to early ERCP (24-72 h) in patients admitted with predicted mild ABP. METHODS: Patients admitted for predicted mild ABP defined as a bedside index of severity in acute pancreatitis score < 3 and underwent ERCP for biliary obstruction within 72 h of presentation during the study period were included. Patients with prior biliary sphincterotomy or surgically altered anatomy preventing conventional ERCP were excluded. The primary outcome was the development of moderately severe or severe pancreatitis based on the revised Atlanta classification. Secondary outcomes were the length of hospital stay, the need for ICU admission, and ERCP-related adverse events (AEs). RESULTS: Of the identified 166 patients, baseline characteristics were similar between both the groups except for the WBC count (9.4 vs. 8.3/µL; p < 0.044) and serum bilirubin level (3.0 vs. 1.6 mg/dL; p < 0.0039). Biliary cannulation rate and technical success were both high in the overall cohort (98.8%). Urgent ERCP was not associated with increased development of moderately severe pancreatitis (10.4% vs. 15.7%; p = 0.3115). The urgent ERCP group had a significantly shorter length of hospital stay [median 3 (IQR 2-3) vs. 3 days (IQR 3-4), p < 0.01]. CONCLUSION: Urgent ERCP did not impact the rate of developing more severe pancreatitis in patients with predicted mild ABP but was associated with a shorter length of hospital stay and a lower rate of hospital readmission.

A Rare Cause of Abdominal Pain: Erdheim-Chester Disease.

A 65-year-old man presented with hematuria, night sweats, nausea, intermittent nonbloody diarrhea, and abdominal pain. Computed tomography angiogram with enterography showed retroperitoneal fibrosis surrounding both kidneys and ureters without any evidence of vascular obstruction or hydronephrosis. Laparoscopic biopsy demonstrated fibroadipose tissue involved by a subtle histiocytic infiltrate in a background of marked fibrosis, scattered lymphocytes, and plasma cells. The histiocytes strongly expressed CD163, Factor XIIIa, and BRAF V600E. He was diagnosed with Erdheim-Chester disease, a rare histiocytic neoplasm uncommonly presenting with gastroenterological manifestations.

Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.

OBJECTIVES: To develop a bounding-box-based 3D convolutional neural network (CNN) for user-guided volumetric pancreas ductal adenocarcinoma (PDA) segmentation. METHODS: Reference segmentations were obtained on CTs (2006-2020) of treatment-naïve PDA. Images were algorithmically cropped using a tumor-centered bounding box for training a 3D nnUNet-based-CNN. Three radiologists independently segmented tumors on test subset, which were combined with reference segmentations using STAPLE to derive composite segmentations. Generalizability was evaluated on Cancer Imaging Archive (TCIA) (n = 41) and Medical Segmentation Decathlon (MSD) (n = 152) datasets. RESULTS: Total 1151 patients [667 males; age:65.3 ± 10.2 years; T1:34, T2:477, T3:237, T4:403; mean (range) tumor diameter:4.34 (1.1-12.6)-cm] were randomly divided between training/validation (n = 921) and test subsets (n = 230; 75% from other institutions). Model had a high DSC (mean ± SD) against reference segmentations (0.84 ± 0.06), which was comparable to its DSC against composite segmentations (0.84 ± 0.11, p = 0.52). Model-predicted versus reference tumor volumes were comparable (mean ± SD) (29.1 ± 42.2-cc versus 27.1 ± 32.9-cc, p = 0.69, CCC = 0.93). Inter-reader variability was high (mean DSC 0.69 ± 0.16), especially for smaller and isodense tumors. Conversely, model's high performance was comparable between tumor stages, volumes and densities (p > 0.05). Model was resilient to different tumor locations, status of pancreatic/biliary ducts, pancreatic atrophy, CT vendors and slice thicknesses, as well as to the epicenter and dimensions of the bounding-box (p > 0.05). Performance was generalizable on MSD (DSC:0.82 ± 0.06) and TCIA datasets (DSC:0.84 ± 0.08). CONCLUSION: A computationally efficient bounding box-based AI model developed on a large and diverse dataset shows high accuracy, generalizability, and robustness to clinically encountered variations for user-guided volumetric PDA segmentation including for small and isodense tumors. CLINICAL RELEVANCE: AI-driven bounding box-based user-guided PDA segmentation offers a discovery tool for image-based multi-omics models for applications such as risk-stratification, treatment response assessment, and prognostication, which are urgently needed to customize treatment strategies to the unique biological profile of each patient's tumor.

Risk of Syndrome-Associated Cancers Among First-Degree Relatives of Patients With Pancreatic Ductal Adenocarcinoma With Pathogenic or Likely Pathogenic Germline Variants.

Importance: Increased 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. Objective: To 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 Participants: This 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 Measures: For 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. Results: In 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 Relevance: In 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.

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.

BACKGROUND: Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited by sampling and variability. We have previously described an artificial intelligence (AI)-aided approach for whole pancreas fat estimation on computed tomography (CT). In this study, we aimed to assess the correlation between whole pancreas MRI proton-density fat fraction (MR-PDFF) and CT attenuation. METHODS: We identified patients without pancreatic disease who underwent both MRI and CT between January 1, 2015 and June 1, 2020. 158 paired MRI and CT scans were available for pancreas segmentation using an iteratively trained convolutional neural network (CNN) with manual correction. Boxplots were generated to visualize slice-by-slice variability in 2D-axial slice MR-PDFF. Correlation between whole pancreas MR-PDFF and age, BMI, hepatic fat and pancreas CT-Hounsfield Unit (CT-HU) was assessed. RESULTS: Mean pancreatic MR-PDFF showed a strong inverse correlation (Spearman -0.755) with mean CT-HU. MR-PDFF was higher in males (25.22 vs 20.87; p = 0.0015) and in subjects with diabetes mellitus (25.95 vs 22.17; p = 0.0324), and was positively correlated with age and BMI. The pancreatic 2D-axial slice-to-slice MR-PDFF variability increased with increasing mean whole pancreas MR-PDFF (Spearman 0.51; p < 0.0001). CONCLUSION: Our study demonstrates a strong inverse correlation between whole pancreas MR-PDFF and CT-HU, indicating that both imaging modalities can be used to assess pancreatic fat. 2D-axial pancreas MR-PDFF is variable across slices, underscoring the need for AI-aided whole-organ measurements for objective and reproducible estimation of pancreatic fat.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

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 cm 3 (20.2 cm 3 ), 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.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Tissue methylated DNA markers for sporadic pancreatic cancer are strongly associated with familial and genetically predisposed pancreatic cancer.

High-risk individuals (HRIs) with familial and genetic predisposition to pancreatic ductal adenocarcinoma (PDAC) are eligible for screening. There is no accurate biomarker for detecting early-stage PDAC. We previously demonstrated that a panel of methylated DNA markers (MDMs) accurately detect sporadic PDAC. In this study we compared the distribution of MDMs in DNA extracted from tissue of PDAC cases who carry germline mutations and non-carriers with family history, with control tissue and demonstrate high discrimination like that seen in sporadic PDAC. These results provide scientific rationale for examining plasma MDMs in HRIs with the goal of developing a minimally-invasive early detection test.

PET/CT and PET/MRI in neuroendocrine neoplasms.

Advanced molecular imaging has come to play an integral role in the management of gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs). Somatostatin receptor (SSTR) PET has now emerged as the reference standard for the evaluation of NENs and is particularly critical in the context of peptide receptor radionuclide therapy (PRRT) eligibility. SSTR PET/MRI with liver-specific contrast agent has a strong potential for one-stop-shop multiparametric evaluation of GEP-NENs. 18F-FDG is a complementary radiotracer to SSTR, especially in the context of high-grade neuroendocrine neoplasms. Knowledge gaps in quantitative evaluation of molecular imaging studies and their role in assessment of response to PRRT and combination therapies are active research areas. Novel radiotracers have the potential to overcome existing limitations in the molecular imaging of GEP-NENs. The purpose of this article is to provide an overview of the current trends, pitfalls, and recent advancements of molecular imaging for GEP-NENs.

Pancreatic cancer risk to siblings of probands in bilineal cancer settings.

PURPOSE: Pancreatic cancer (PC) risk is increased in families, but PC risk and risk perception have been understudied when both parents have cancer. METHODS: An unbiased method defining cancer triads (proband with PC and both parents with cancer) in a prospective registry estimated risk of PC to probands' siblings in triad group 1 (no parent with PC), group 2 (1 parent with PC), and group 3 (both parents with PC). We estimated standardized incidence ratios (SIRs) using a Surveillance, Epidemiology, and End Results (SEER) reference. We also estimated the risk when triad probands carried germline pathogenic/likely pathogenic variants in any of the 6 PC-associated genes (ATM, BRCA1, BRCA2, CDKN2A, MLH1, and TP53). PC risk perception/concern was surveyed in siblings and controls. RESULTS: Risk of PC was higher (SIR = 3.5; 95% CI = 2.2-5.2) in 933 at-risk siblings from 297 triads. Risk increased by triad group: 2.8 (95% CI = 1.5-4.5); 4.5 (95% CI = 1.6-9.7); and 21.2 (95% CI = 4.3-62.0). SIR in variant-negative triads was 3.0 (95% CI = 1.6-5.0), whereas SIR in variant-positive triads was 10.0 (95% CI = 3.2-23.4). Siblings' perceived risk/concern of developing PC increased by triad group. CONCLUSION: Sibling risks were 2.8- to 21.2-fold higher than that of the general population. Positive variant status increased the risk in triads. Increasing number of PC cases in a triad was associated with increased concern and perceived PC risk.

Prospective Study Assessing Impact of Ethylene Oxide Sterilization on Endoscopic Ultrasound Image Quality.

BACKGROUND & AIMS: Duodenoscope-associated transmission of infections has raised questions about efficacy of endoscope reprocessing using high-level disinfection (HLD). Although ethylene oxide (ETO) gas sterilization is effective in eradicating microbes, the impact of ETO on endoscopic ultrasound (EUS) imaging equipment remains unknown. In this study, we aimed to compare the changes in EUS image quality associated with HLD vs HLD followed by ETO sterilization. METHODS: Four new EUS instruments were assigned to 2 groups: Group 1 (HLD) and Group 2 (HLD + ETO). The echoendoscopes were assessed at baseline, monthly for 6 months, and once every 3 to 4 months thereafter, for a total of 12 time points. At each time point, review of EUS video and still image quality was performed by an expert panel of reviewers along with phantom-based objective testing. Linear mixed effects models were used to assess whether the modality of reprocessing impacted image and video quality. RESULTS: For clinical testing, mixed linear models showed minimal quantitative differences in linear analog score (P = .04; estimated change, 3.12; scale, 0-100) and overall image quality value (P = .007; estimated change, -0.12; scale, 1-5) favoring ETO but not for rank value (P = .06). On phantom testing, maximum depth of penetration was lower for ETO endoscopes (P < .001; change in depth, 0.49 cm). CONCLUSIONS: In this prospective study, expert review and phantom-based testing demonstrated minimal differences in image quality between echoendoscopes reprocessed using HLD vs ETO + HLD over 2 years of clinical use. Further studies are warranted to assess the long-term clinical impact of these findings. In the interim, these results support use of ETO sterilization of EUS instruments if deemed clinically necessary.

Chronic Pancreatitis Is a Risk Factor for Pancreatic Cancer, and Incidence Increases With Duration of Disease: A Systematic Review and Meta-analysis.

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.

Determining age and sex-specific distribution of pancreatic whole-gland CT attenuation using artificial intelligence aided image segmentation: Associations with body composition and pancreatic cancer risk.

BACKGROUND & AIMS: Increased intrapancreatic fat is associated with pancreatic diseases; however, there are no established objective diagnostic criteria for fatty pancreas. On non-contrast computed tomography (CT), adipose tissue shows negative Hounsfield Unit (HU) attenuations (-150 to -30 HU). Using whole organ segmentation on non-contrast CT, we aimed to describe whole gland pancreatic attenuation and establish 5th and 10th percentile thresholds across a spectrum of age and sex. Subsequently, we aimed to evaluate the association between low pancreatic HU and risk of pancreatic ductal adenocarcinoma (PDAC). METHODS: The whole pancreas was segmented in 19,456 images from 469 non-contrast CT scans. A convolutional neural network was trained to assist pancreas segmentation. Mean pancreatic HU, volume, and body composition metrics were calculated. The lower 5th and 10th percentile for mean pancreatic HU were identified, examining the association with age and sex. Pre-diagnostic CT scans from patients who later developed PDAC were compared to cancer-free controls. RESULTS: Less than 5th percentile mean pancreatic HU was significantly associated with increase in BMI (OR 1.07; 1.03-1.11), visceral fat (OR 1.37; 1.15-1.64), total abdominal fat (OR 1.12; 1.03-1.22), and diabetes mellitus type 1 (OR 6.76; 1.68-27.28). Compared to controls, pre-diagnostic scans in PDAC cases had lower mean whole gland pancreatic HU (-0.2 vs 7.8, p = 0.026). CONCLUSION: In this study, we report age and sex-specific distribution of pancreatic whole-gland CT attenuation. Compared to controls, mean whole gland pancreatic HU is significantly lower in the pre-diagnostic phase of PDAC.