Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst.

BACKGROUND/OBJECTIVES: Pancreatic cyst management can be distilled into three separate pathways - discharge, monitoring or surgery- based on the risk of malignant transformation. This study compares the performance of artificial intelligence (AI) models to clinical care for this task. METHODS: Two explainable boosting machine (EBM) models were developed and evaluated using clinical features only, or clinical features and cyst fluid molecular markers (CFMM) using a publicly available dataset, consisting of 850 cases (median age 64; 65 % female) with independent training (429 cases) and holdout test cohorts (421 cases). There were 137 cysts with no malignant potential, 114 malignant cysts, and 599 IPMNs and MCNs. RESULTS: The EBM and EBM with CFMM models had higher accuracy for identifying patients requiring monitoring (0.88 and 0.82) and surgery (0.66 and 0.82) respectively compared with current clinical care (0.62 and 0.58). For discharge, the EBM with CFMM model had a higher accuracy (0.91) than either the EBM model (0.84) or current clinical care (0.86). In the cohort of patients who underwent surgical resection, use of the EBM-CFMM model would have decreased the number of unnecessary surgeries by 59 % (n = 92), increased correct surgeries by 7.5 % (n = 11), identified patients who require monitoring by 122 % (n = 76), and increased the number of patients correctly classified for discharge by 138 % (n = 18) compared to clinical care. CONCLUSIONS: EBM models had greater sensitivity and specificity for identifying the correct management compared with either clinical management or previous AI models. The model predictions are demonstrated to be interpretable by clinicians.

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening. MATERIALS AND METHODS: Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses. RESULTS: A total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20-85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5-1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80-98), 76 % specificity (95 % CI: 62-88) and an AUC of 0.87 (95 % CI: 0.79-0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79-0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images. CONCLUSION: Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.

Diminishing calcifications as a potential predictor of pancreatic ductal adenocarcinoma arising in association with IPMN in patients with chronic pancreatitis.

Chronic pancreatitis (CP) is a progressive benign fibroinflammatory condition involving repeated episodes of pancreatic inflammation, which lead to fibrotic tissue replacement and subsequent pancreatic insufficiency. A lifetime risk of developing pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis is reported to be 1.5%-4%. However, diagnosis of PDAC in patients with CP can be challenging, in part due to overlapping imaging features. In rare instances, pancreatic parenchymal calcifications that are typically associated with chronic pancreatitis may diminish in the case of a developing PDAC. In this article, we present a patient with chronic pancreatitis in whom calcifications decreased at the time of pancreatic ductal adenocarcinoma diagnosis, as compared to prior CT imaging. The unique imaging features of "diminishing calcifications" associated with a hypoattenuating lesion can potentially be a useful sign of pancreatic ductal adenocarcinoma and may aid in early diagnosis and prompt treatment intervention.

Preoperative Prediction of Lymph Node Metastases in Nonfunctional Pancreatic Neuroendocrine Tumors Using a Combined CT Radiomics-Clinical Model.

BACKGROUND: PanNETs are a rare group of pancreatic tumors that display heterogeneous histopathological and clinical behavior. Nodal disease has been established as one of the strongest predictors of patient outcomes in PanNETs. Lack of accurate preoperative assessment of nodal disease is a major limitation in the management of these patients, in particular those with small (< 2 cm) low-grade tumors. The aim of the study was to evaluate the ability of radiomic features (RF) to preoperatively predict the presence of nodal disease in pancreatic neuroendocrine tumors (PanNETs). PATIENTS AND METHODS: An institutional database was used to identify patients with nonfunctional PanNETs undergoing resection. Pancreas protocol computed tomography was obtained, manually segmented, and RF were extracted. These were analyzed using the minimum redundancy maximum relevance analysis for hierarchical feature selection. Youden index was used to identify the optimal cutoff for predicting nodal disease. A random forest prediction model was trained using RF and clinicopathological characteristics and validated internally. RESULTS: Of the 320 patients included in the study, 92 (28.8%) had nodal disease based on histopathological assessment of the surgical specimen. A radiomic signature based on ten selected RF was developed. Clinicopathological characteristics predictive of nodal disease included tumor grade and size. Upon internal validation the combined radiomics and clinical feature model demonstrated adequate performance (AUC 0.80) in identifying nodal disease. The model accurately identified nodal disease in 85% of patients with small tumors (< 2 cm). CONCLUSIONS: Non-invasive preoperative assessment of nodal disease using RF and clinicopathological characteristics is feasible.

Early detection of pancreatic cancer in the era of precision medicine.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Hidden in plain sight: commonly missed early signs of pancreatic cancer on CT.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis mostly due to the advanced stage at which disease is diagnosed. Early detection of disease at a resectable stage is, therefore, critical for improving outcomes of patients. Prior studies have demonstrated that pancreatic abnormalities may be detected on CT in up to 38% of CT studies 5 years before clinical diagnosis of PDAC. In this review, we highlight commonly missed signs of early PDAC on CT. Broadly, these commonly missed signs consist of small isoattenuating PDAC without contour deformity, isolated pancreatic duct dilatation and cutoff, focal pancreatic enhancement and focal parenchymal atrophy, pancreatitis with underlying PDAC, and vascular encasement. Through providing commentary on demonstrative examples of these signs, we demonstrate how to reduce the risk of missing or misinterpreting radiological features of early PDAC.

Factors associated with radiological misstaging of pancreatic ductal adenocarcinoma: A retrospective observational study.

PURPOSE: Accurate staging of disease is vital in determining appropriate care for patients with pancreatic ductal adenocarcinoma (PDAC). It has been shown that the quality of scans and the experience of a radiologist can impact computed tomography (CT) based assessment of disease. The aim of the current study was to evaluate the impact of the rereading of outside hospital (OH) CT by an expert radiologist and a repeat pancreatic protocol CT (PPCT) on staging of disease. METHODS: Patients evaluated at the our institute's pancreatic multidisciplinary clinic (2006 to 2014) with OH scan and repeat PPCT performed within 30 days were included. In-house radiologists staged disease using OH scans and repeat PPCT, and factors associated with misstaging were determined. RESULTS: The study included 100 patients, with a median time between OH scan and PPCT of 19 days (IQR: 13-23 days.) Stage migration was mostly accounted for by upstaging of disease (58.8 % to 83.3 %) in all comparison groups. When OH scans were rereviewed, 21.5 % of the misstaging was due to missed metastases, however, when rereads were compared to the PPCT, occult metastases accounted for the majority of misstaged patients (62.5 %). Potential factors associated with misstaging were primarily related to imaging technique. CONCLUSION: A repeat PPCT results in increased detection of metastatic disease that rereviews of OH scans may otherwise miss. Accessible insurance coverage for repeat PPCT imaging even within 30 days of an OH scan could help optimize delivery of care and alleviate burdens associated with misstaging.

Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature.

PURPOSE: The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs). MATERIALS AND METHODS: A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference. RESULTS: A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3-85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71-0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7-95.3) and 73.3% (95% CI: 62.9-83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001). CONCLUSION: Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.

Deep neural network-based segmentation of normal and abnormal pancreas on abdominal CT: evaluation of global and local accuracies.

PURPOSE: Delay in diagnosis can contribute to poor outcomes in pancreatic ductal adenocarcinoma (PDAC), and new tools for early detection are required. Recent application of artificial intelligence to cancer imaging has demonstrated great potential in detecting subtle early lesions. The aim of the study was to evaluate global and local accuracies of deep neural network (DNN) segmentation of normal and abnormal pancreas with pancreatic mass. METHODS: Our previously developed and reported residual deep supervision network for segmentation of PDAC was applied to segment pancreas using CT images of potential renal donors (normal pancreas) and patients with suspected PDAC (abnormal pancreas). Accuracy of DNN pancreas segmentation was assessed using DICE simulation coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance 95% percentile (HD95) as compared to manual segmentation. Furthermore, two radiologists semi-quantitatively assessed local accuracies and estimated volume of correctly segmented pancreas. RESULTS: Forty-two normal and 49 abnormal CTs were assessed. Average DSC was 87.4 ± 3.1% and 85.5 ± 3.2%, ASSD 0.97 ± 0.30 and 1.34 ± 0.65, HD95 4.28 ± 2.36 and 6.31 ± 6.31 for normal and abnormal pancreas, respectively. Semi-quantitatively, ≥95% of pancreas volume was correctly segmented in 95.2% and 53.1% of normal and abnormal pancreas by both radiologists, and 97.6% and 75.5% by at least one radiologist. Most common segmentation errors were made on pancreatic and duodenal borders in both groups, and related to pancreatic tumor including duct dilatation, atrophy, tumor infiltration and collateral vessels. CONCLUSION: Pancreas DNN segmentation is accurate in a majority of cases, however, minor manual editing may be necessary; particularly in abnormal pancreas.

Radiologists' Expectations of Artificial Intelligence in Pancreatic Cancer Imaging: How Good Is Good Enough?

BACKGROUND: Existing (artificial intelligence [AI]) tools in radiology are modeled without necessarily considering the expectations and experience of the end user-the radiologist. The literature is scarce on the tangible parameters that AI capabilities need to meet for radiologists to consider them useful tools. OBJECTIVE: The purpose of this study is to explore radiologists' attitudes toward AI tools in pancreatic cancer imaging and to quantitatively assess their expectations of these tools. METHODS: A link to the survey was posted on the www.ctisus.com website, advertised in the www.ctisus.com email newsletter, and publicized on LinkedIn, Facebook, and Twitter accounts. This survey asked participants about their demographics, practice, and current attitudes toward AI. They were also asked about their expectations of what constitutes a clinically useful AI tool. The survey consisted of 17 questions, which included 9 multiple choice questions, 2 Likert scale questions, 4 binary (yes/no) questions, 1 rank order question, and 1 free text question. RESULTS: A total of 161 respondents completed the survey, yielding a response rate of 46.3% of the total 348 clicks on the survey link. The minimum acceptable sensitivity of an AI program for the detection of pancreatic cancer chosen by most respondents was either 90% or 95% at a specificity of 95%. The minimum size of pancreatic cancer that most respondents would find an AI useful at detecting was 5 mm. Respondents preferred AI tools that demonstrated greater sensitivity over those with greater specificity. Over half of respondents anticipated incorporating AI tools into their clinical practice within the next 5 years. CONCLUSION: Radiologists are open to the idea of integrating AI-based tools and have high expectations regarding the performance of these tools. Consideration of radiologists' input is important to contextualize expectations and optimize clinical adoption of existing and future AI tools.

Extramedullary Hematopoiesis: A Forgotten Diagnosis and a Great Mimicker of Malignancy.

ABSTRACT: Radiology errors have been reported in up to 30% of cases when patients have abnormal imaging findings. Although more than half of errors are failures to detect critical findings, over 40% of errors are when findings are recognized but the correct diagnosis or interpretation is not made. One common source of error is when imaging findings from one process simulate imaging findings from another process but the correct diagnosis is not made. This can result in additional imaging studies, unnecessary biopsies, or surgery. Extramedullary hematopoiesis is one of those uncommon disease processes that can produce many imaging findings that may lead to misdiagnosis. The objective of this article is to review the common and uncommon imaging features of extramedullary hematopoiesis while presenting a series of interesting relevant illustrative cases with emphasis on CT.

A primer on artificial intelligence in pancreatic imaging.

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists.

PURPOSE: A wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist. METHODS: In this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis. RESULTS: 214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist. CONCLUSION: Radiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.

The Impact of the COVID-19 Pandemic on Multidisciplinary Clinics: A High-Volume Pancreatic Cancer Center Experience.

The unprecedented impact of the Sars-CoV-2 pandemic (COVID-19) has strained the healthcare system worldwide. The impact is even more profound on diseases requiring timely complex multidisciplinary care such as pancreatic cancer. Multidisciplinary care teams have been affected significantly in multiple ways as healthcare teams collectively acclimate to significant space limitations and shortages of personnel and supplies. As a result, many patients are now receiving suboptimal remote imaging for diagnosis, staging, and surgical planning for pancreatic cancer. In addition, the lack of face-to-face interactions between the physician and patient and between multidisciplinary teams has challenged patient safety, research investigations, and house staff education. In this study, we discuss how the COVID-19 pandemic has transformed our high-volume pancreatic multidisciplinary clinic, the unique challenges faced, as well as the potential benefits that have arisen out of this situation. We also reflect on its implications for the future during and beyond the pandemic as we anticipate a hybrid model that includes a component of virtual multidisciplinary clinics as a means to provide accessible world-class healthcare for patients who require complex oncologic management.

The Impact of Clinical and Pathological Features on Intraductal Papillary Mucinous Neoplasm Recurrence After Surgical Resection: Long-Term Follow-Up Analysis.

OBJECTIVE: This study aimed to identify risk factors for recurrence after pancreatic resection for intraductal papillary mucinous neoplasm (IPMN). SUMMARY BACKGROUND DATA: Long-term follow-up data on recurrence after surgical resection for IPMN are currently lacking. Previous studies have presented mixed results on the role of margin status in risk of recurrence after surgical resection. METHODS: A total of 126 patients that underwent resection for noninvasive IPMN were followed for a median of 9.5 years. Dedicated pathological and radiological reviews were performed to correlate clinical and pathological features (including detailed pathological features of the parenchymal margin) with recurrence after surgical resection. In addition, in a subset of 32 patients with positive margins, we determined the relationship between the margin and original IPMN using driver gene mutations identified by next-generation sequencing. RESULTS: Family history of pancreatic cancer and high-grade IPMN was identified as risk factors for recurrence in both uni- and multivariate analysis (adjusted hazard ratio 3.05 and 1.88, respectively). Although positive margin was not significantly associated with recurrence in our cohort, the size and grade of the dysplastic focus at the margin were significantly correlated with recurrence in margin-positive patients. Genetic analyses showed that the neoplastic epithelium at the margin was independent from the original IPMN in at least 9 of 32 cases (28%). The majority of recurrences (74%) occurred after 3 years, and a significant minority (32%) occurred after 5 years. CONCLUSION: Sustained postoperative surveillance for all patients is indicated, particularly those with risk factors such has family history and high-grade dysplasia.

CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.

OBJECTIVE. Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. MATERIALS AND METHODS. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified. Demographic, clinical, and survival information was collected from the medical records. Survival time after the surgical resection was used to stratify patients into a low-risk group (survival time > 3 years) and a high-risk group (survival time < 1 year). The 3D volume of the whole pancreatic tumor and background pancreas were manually segmented. A total of 478 radiomics features were extracted from tumors and 11 extra features were computed from pancreas boundaries. The 10 most relevant features were selected by feature reduction. Survival analysis was performed on the basis of clinical parameters both with and without the addition of the selected features. Survival status and time were estimated by a random survival forest algorithm. Concordance index (C-index) was used to evaluate performance of the survival prediction model. RESULTS. The mean age of patients with PDAC was 67 ± 11 (SD) years. The mean tumor size was 3.31 ± 2.55 cm. The 10 most relevant radiomics features showed 82.2% accuracy in the classification of high-risk versus low-risk groups. The C-index of survival prediction with clinical parameters alone was 0.6785. The addition of CT radiomics features improved the C-index to 0.7414. CONCLUSION. Addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC.

Current Status of Radiomics and Deep Learning in Liver Imaging.

ABSTRACT: Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.

Protein synthesis inhibitor omacetaxine is effective against hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth most common and the fourth most deadly cancer worldwide. The development cost of new therapeutics is a major limitation in patient outcomes. Importantly, there is a paucity of preclinical HCC models in which to test new small molecules. Herein, we implemented potentially novel patient-derived organoid (PDO) and patient-derived xenografts (PDX) strategies for high-throughput drug screening. Omacetaxine, an FDA-approved drug for chronic myelogenous leukemia (CML), was found to be a top effective small molecule in HCC PDOs. Next, omacetaxine was tested against a larger cohort of 40 human HCC PDOs. Serial dilution experiments demonstrated that omacetaxine is effective at low (nanomolar) concentrations. Mechanistic studies established that omacetaxine inhibits global protein synthesis, with a disproportionate effect on short-half-life proteins. High-throughput expression screening identified molecular targets for omacetaxine, including key oncogenes, such as PLK1. In conclusion, by using an innovative strategy, we report - for the first time to our knowledge - the effectiveness of omacetaxine in HCC. In addition, we elucidate key mechanisms of omacetaxine action. Finally, we provide a proof-of-principle basis for future studies applying drug screening PDOs sequenced with candidate validation in PDX models. Clinical trials could be considered to evaluate omacetaxine in patients with HCC.

Hepatic angiosarcoma in an adult who had Wilms tumor treated in childhood: A case report.

Hepatic angiosarcoma is a rare, highly aggressive mesenchymal liver malignancy with poor prognosis that stems from the endothelial cells that line the walls of blood or lymphatic vessels. It is the third most common primary liver malignancy and is most prevalent among older males. It is difficult to diagnose due to various clinical presentations from asymptomatic to abdominal pain, pleural effusion, and liver failure. The diagnosis of liver angiosarcoma is suspected on imaging features and confirmed by histopathological assessment. Primary management is determined based on the stage of tumor from surgery to palliative care such as chemotherapy or tumor transarterial embolization. We report a 51-year-old female who presented with stage 4 liver angiosarcoma and a history of childhood Wilms tumor. We focus on tumor management using radiological modalities and pathological analysis and discuss secondary liver tumors in survivors of childhood Wilms tumor.

Pancreatic Cancer Imaging: A New Look at an Old Problem.

Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.