Postoperative surveillance of pancreatic ductal adenocarcinoma (PDAC) recurrence: practice pattern on standardized imaging and reporting from the society of abdominal radiology disease focus panel on PDAC.

PURPOSE: Surgical resection is the only potential curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), but unfortunately most patients recur within 5 years of surgery. This article aims to assess the practice patterns across major academic institutions and develop consensus recommendations for postoperative imaging and interpretation in patients with PDAC. METHODS: The consensus recommendations for postoperative imaging surveillance following PDAC resection were developed using the Delphi method. Members of the Society of Abdominal Radiology (SAR) PDAC Disease Focused Panel (DFP) underwent three rounds of surveys followed by live webinar group discussions to develop consensus recommendations. RESULTS: Significant variations currently exist in the postoperative surveillance of PDAC, even among academic institutions. Differentiating common postoperative inflammatory and fibrotic changes from tumor recurrence remains a diagnostic challenge, and there is no reliable size threshold or growth rate of imaging findings that can provide differentiation. A new liver lesion or peritoneal nodule should be considered suspicious for tumor recurrence, and the imaging features should be interpreted in the appropriate clinical context (e.g., CA 19-9, clinical presentation, pathologic staging). CONCLUSION: Postoperative imaging following PDAC resection is challenging to interpret due to the presence of confounding postoperative inflammatory changes. A standardized reporting template for locoregional findings and report impression may improve communication of relaying risk of recurrence with referring providers, which merits validation in future studies.

Baseline CT-based Radiomic Features Aid Prediction of Nodal Positivity after Neoadjuvant Therapy in Pancreatic Cancer.

Purpose To study the association between CT-derived textural features of pancreatic cancer and patient outcome. Materials and Methods This retrospective study evaluated 54 patients (median age, 62 years [range, 40-88 years]; 32 men) with pancreatic cancer who underwent chemoradiation followed by surgical resection and lymph node dissection from May 2012 to June 2016. Three-dimensional segmentation of the pancreatic tumor was performed on baseline dual-energy CT images: 70-keV pancreatic parenchymal phase (PPP) images and iodine material density images. Then, 15 and 19 radiomic features were extracted from each phase, respectively. Logistic regression with elastic net regularization was used to select textural features associated with outcome, and receiver operating characteristic analysis evaluated feature performance. Survival curves were generated using the Kaplan-Meier method. Results The feature of integral total (∫ T), representing the mean intensity in Hounsfield units times the contour volume in milliliters of PPP imaging (hereafter, "∫ T (HU·mL) (PPP)"), is inversely associated with posttherapy pathologic lymph node (ypN) category. A threshold ∫ T (HU·mL) (PPP) less than 507.85 predicted ypN1-2 classification with 96% sensitivity, 34% specificity, and area under the curve of 0.61. Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased overall survival (median, 2.8 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (one event at 3.4 years) (P = .006). Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased progression-free survival (median, 1.5 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (median, 2.7 years) (P = .001). Conclusion A CT-based radiomic signature may help predict ypN category in patients with pancreatic cancer. Keywords: CT-Dual Energy, Abdomen/GI, Pancreas, Tumor Response, Outcomes Analysis © RSNA, 2022 Supplemental material is available for this article.

PIONEER-Panc: a platform trial for phase II randomized investigations of new and emerging therapies for localized pancreatic cancer.

BACKGROUND: Personalized and effective treatments for pancreatic ductal adenocarcinoma (PDAC) continue to remain elusive. Novel clinical trial designs that enable continual and rapid evaluation of novel therapeutics are needed. Here, we describe a platform clinical trial to address this unmet need. METHODS: This is a phase II study using a Bayesian platform design to evaluate multiple experimental arms against a control arm in patients with PDAC. We first separate patients into three clinical stage groups of localized PDAC (resectable, borderline resectable, and locally advanced disease), and further divide each stage group based on treatment history (treatment naïve or previously treated). The clinical stage and treatment history therefore define 6 different cohorts, and each cohort has one control arm but may have one or more experimental arms running simultaneously. Within each cohort, adaptive randomization rules are applied and patients will be randomized to either an experimental arm or the control arm accordingly. The experimental arm(s) of each cohort are only compared to the applicable cohort specific control arm. Experimental arms may be added independently to one or more cohorts during the study. Multiple correlative studies for tissue, blood, and imaging are also incorporated. DISCUSSION: To date, PDAC has been treated as a single disease, despite knowledge that there is substantial heterogeneity in disease presentation and biology. It is recognized that the current approach of single arm phase II trials and traditional phase III randomized studies are not well-suited for more personalized treatment strategies in PDAC. The PIONEER Panc platform clinical trial is designed to overcome these challenges and help advance our treatment strategies for this deadly disease. TRIAL REGISTRATION: This study is approved by the Institutional Review Board (IRB) of MD Anderson Cancer Center, IRB-approved protocol 2020-0075. The PIONEER trial is registered at the US National Institutes of Health (ClinicalTrials.gov) NCT04481204 .

NBTXR3, a first-in-class radioenhancer for pancreatic ductal adenocarcinoma: Report of first patient experience.

BACKGROUND AND PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) remains one of the leading causes of cancer-related deaths in the world. For patients with PDAC who are not eligible for surgery, radiation therapy improves local disease control, yet safely delivering therapeutic doses of radiation remains challenging due to off-target toxicities in surrounding normal tissues. NBTXR3, a novel radioenhancer composed of functionalized hafnium oxide crystalline nanoparticles, has recently shown clinical activity in soft tissue sarcoma, hepatocellular carcinoma, head and neck squamous cell carcinoma, and advanced solid malignancies with lung or liver metastases. Here we report the first patient with pancreatic cancer treated with NBTXR3. MATERIALS AND METHODS: A 66-year-old male with unresectable locally advanced PDAC was enrolled on our clinical trial to receive NBTXR3 activated by radiation therapy. Local endoscopic delivery of NBTXR3 was followed by intensity modulated radiation therapy (IMRT). Follow-up assessment consisted of physical examination, laboratory studies including CA19-9, and CT of the chest, abdomen, and pelvis. RESULTS: The patient received NBTXR3 by local endoscopic delivery without any acute adverse events. Radiation treatment consisted of 45 Gy in 15 daily fractions using IMRT. The patient began radiation twelve days after NBTXR3 injection. Daily CT-on-rails imaging demonstrated retention of NBTXR3 within the tumor for the duration of treatment. At initial follow-up evaluation, the lesion remained radiographically stable and the patient did not demonstrate treatment-related toxicity. CONCLUSION: This report demonstrates initial feasibility of local endoscopic delivery of NBTXR3 activated by radiation therapy for patients with pancreatic cancer who are not eligible for surgery.

Update on quantitative radiomics of pancreatic tumors.

Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.

CT features predictive of nodal positivity at surgery in pancreatic cancer patients following neoadjuvant therapy in the setting of dual energy CT.

PURPOSE: Evaluate utility of dual energy CT iodine material density images to identify preoperatively nodal positivity in pancreatic cancer patients who underwent neoadjuvant therapy. METHODS: This IRB approved retrospective study evaluated 62 patients between 2012 and 2016 with proven pancreatic ductal adenocarcinoma, who underwent neoadjuvant therapy, tumor resection and both baseline and preoperative assessment with pancreatic multiphasic rapid switching dual energy CT. Three radiologists in consensus identified on imaging nodes > 0.5 cm in short axis, evaluated nodal morphology, size and on each phase density in HU, and concentrations on iodine material density images normalized to the aorta. RESULTS: Of 62 patients, 33 were N0, 20 N1, and 9 N2. Total of 145 lymph nodes were evaluated, with average number of nodes per anatomic site ranging from 1.3 (body tumors) to 5 (uncinate) versus average of 24 and 30 nodes recovered respectively at surgery. Most (N = 44) were pancreatic head tumors. For all patients, regardless of site of primary tumor, the minimum measured iodine value of all of a patient's measured nodes taken as a group on preoperative studies, as normalized to the aorta, was significant at P = 0.041 value in differentiating N0 from N1/2 and ROC analysis showed an AUC of 0.67. With a cutoff of 0.2857, sensitivity was 0.78 and specificity was 0.58, with values < 0.2857 indicative of N1/2. Node morphology and changes in nodal size weren't statistically significant. CONCLUSION: The dual energy based minimum normalized iodine value of all nodes in the surgical field on preoperative studies has modest utility in differentiating N0 from N1/2, and generally outperformed conventional features for identifying nodal metastases.

Clinicopathological correlation of radiologic measurement of post-therapy tumor size and tumor volume for pancreatic ductal adenocarcinoma.

OBJECTIVES: Tumor size measurement is critical for accurate tumor staging in patients with pancreatic ductal adenocarcinoma (PDAC). However, accurate tumor size measurement is challenging in patients who received neoadjuvant therapy before resection, due to treatment-induced fibrosis and tumor invasion beyond the grossly identified tumor area. In this study, we evaluated the correlation between the tumor size and tumor volume measured on post-therapy computed tomography (CT) scans and the pathological measurement. Also, we investigated the correlation between these measurements and clinicopathological parameters and survival. MATERIALS AND METHODS: Retrospectively, we evaluated 343 patients with PDAC who received neoadjuvant therapy, followed by pancreaticoduodenectomy and had pre-operative pancreatic protocol CT imaging. We measured the longest tumor diameter (RadL) and the radiological tumor volume (RadV) on the post-therapy CT scan, then we categorized RadL into four radiologic tumor stages (RTS) based on the current AJCC staging (8th edition) protocol and RadV based on the median. Pearson correlation or Spearman's coefficient (δ), T-test and ANOVA was used to test the correlation between the radiological and pathological measurement. Chi-square analysis was used to test the correlation with the tumor pathological response, lymph-node metastasis and margin status and Kaplan-Meier and Cox-proportional hazard for survival analysis. P-value < 0.05 was considered significant. RESULTS: As a continuous variable, RadL showed a positive linear correlation with the post-therapy pathologic tumor size in the overall patient population (Pearson correlation coefficient: 0.72, P < 0.001) and RadV (δ: 0.63, p < 0.0001). However, there was no correlation between RadL and pathologic tumor size in patients with ypT0 and those with pathologic tumor size of ≤1.0 cm. Post-therapy RTS and RadV group correlated with ypT stage, tumor response grades using either CAP or MDA grading system, distance of superior mesenteric artery margin and tumor recurrence/metastasis. CONCLUSION: Although RadL tends to understage ypT in PDAC patients who had no radiologically detectable tumor or small tumors (RTS0 or RTS1), radiologic measurement of post-therapy tumor size may be used as a marker for the pathologic tumor staging and tumor response to neoadjuvant therapy.

Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma Exhibit Differential Growth and Metabolic Patterns in the Pre-Diagnostic Period: Implications for Early Detection.

BACKGROUND: Previously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis. MATERIALS AND METHODS: Retrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value <0.05 was considered significant. RESULTS: Compared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month-1 vs. 0.088 month-1, p<0.0001) and longer average initiation-times (14 years vs. 5 years, p<0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month-1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p <0.001), and BG disturbance (p<0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p<0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors. CONCLUSION: Imaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.

Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study.

Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.

Therapeutic response assessment in pancreatic ductal adenocarcinoma: society of abdominal radiology review paper on the role of morphological and functional imaging techniques.

Pancreatic ductal adenocarcinoma (PDA) is the third leading cause of cancer-related death in the United States and is projected to be the second by 2030. Systemic combination chemotherapy is considered an essential first-line treatment for the majority of patients with PDA, in both the neoadjuvant and palliative settings. In addition, a number of novel therapies are being tested in clinical trials for patients with advanced PDA. In all cases, accurate and timely assessment of treatment response is critical to guide therapy, reduce drug toxicities and cost from a failing therapy, and aid adaptive clinical trials. Conventional morphological imaging has significant limitations, especially in the context of determining primary tumor response and resectability following neoadjuvant therapies. In this article, we provide an overview of current therapy options for PDA, highlight several morphological imaging findings that may be helpful to reduce over-staging following neoadjuvant therapy, and discuss a number of emerging imaging, and non-imaging, tools that have shown promise in providing a more precise quantification of disease burden and treatment response in PDA.

Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement.

Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Quantitative attenuation accuracy of virtual non-enhanced imaging compared to that of true non-enhanced imaging on dual-source dual-energy CT.

PURPOSE: To evaluate the quantitative attenuation and reliability of virtual non-contrast (VNC) images of the abdomen acquired from multiphasic scans with a dual-energy computed tomography (DECT) system and compare it with that of true non-enhanced images (TNC) on second- (Flash) and third- (Force) generation DECT scanners. METHODS: This retrospective study was approved by the institutional review board and included 123 patients with pancreatic cancer who had undergone routine clinical multiphasic DECT examinations at our institution using Flash and Force scanners between March and August 2017. VNC images of the abdomen were reconstructed from late arterial phase images. For every patient, regions-of-interest were defined in the aorta, fluid-containing structures (gallbladder, pleural effusion, and renal cysts > 10 mm), paravertebral muscles, subcutaneous fat, spleen, pancreas, renal cortex, and liver (eight locations) on TNC and VNC images. The mean attenuation of VNC was compared with TNC by organ for each CT scanner using an equivalence test and the Bland-Altman plot. The mean attenuations for TNC or VNC were compared between the Force and Flash CT scanners using a two-sample t test. RESULTS: The VNC attenuation of organs on the Force scanner was lower than was that on the Flash, and the mean attenuation difference in different organs on the Force was closer to 0. The estimated means of TNC and VNC were equivalent for an equivalence margin of 10 on the Force scanner. CONCLUSION: VNC images in DECT are a promising alternative to TNC images. In clinical scenarios in which non-enhanced CT images are required but are not available for accurate diagnosis, VNC images can potentially serve as an alternative to TNC images without the radiation exposure risks.

Enhancement pattern mapping technique for improving contrast-to-noise ratios and detectability of hepatobiliary tumors on multiphase computed tomography.

PURPOSE: Currently, radiologists use tumor-to-normal tissue contrast across multiphase computed tomography (MPCT) for lesion detection. Here, we developed a novel voxel-based enhancement pattern mapping (EPM) technique and investigated its ability to improve contrast-to-noise ratios (CNRs) in a phantom study and in patients with hepatobiliary cancers. METHODS: The EPM algorithm is based on the root mean square deviation between each voxel and a normal liver enhancement model using patient-specific (EPM-PA) or population data (EPM-PO). We created a phantom consisting of liver tissue and tumors with distinct enhancement signals under varying tumor sizes, motion, and noise. We also retrospectively evaluated 89 patients with hepatobiliary cancers who underwent active breath-hold MPCT between 2016 and 2017. MPCT phases were registered using a three-dimensional deformable image registration algorithm. For the patient study, CNRs of tumor to adjacent tissue across MPCT phases, EPM-PA and EPM-PO were measured and compared. RESULTS: EPM resulted in statistically significant CNR improvement (P < 0.05) for tumor sizes down to 3 mm, but the CNR improvement was significantly affected by tumor motion and image noise. Eighty-two of 89 hepatobiliary cases showed CNR improvement with EPM (PA or PO) over grayscale MPCT, by an average factor of 1.4, 1.6, and 1.5 for cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis, respectively (P < 0.05 for all). CONCLUSIONS: EPM increases CNR compared with grayscale MPCT for primary and secondary hepatobiliary cancers. This new visualization method derived from MPCT datasets may have applications for early cancer detection, radiomic characterization, tumor treatment response, and segmentation. IMPLICATIONS FOR PATIENT CARE: We developed a voxel-wise enhancement pattern mapping (EPM) technique to improve the contrast-to-noise ratio (CNR) of multiphase CT. The improvement in CNR was observed in datasets of patients with cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis. EPM has the potential to be clinically useful for cancers with regard to early detection, radiomic characterization, response, and segmentation.

An open-label, single-arm pilot study of EUS-guided brachytherapy with phosphorus-32 microparticles in combination with gemcitabine +/- nab-paclitaxel in unresectable locally advanced pancreatic cancer (OncoPaC-1): Technical details and study protocol.

Current treatment options for patients with unresectable locally advanced pancreatic cancer (LAPC) include chemotherapy alone or followed by chemoradiation or stereotactic body radiotherapy. However, the prognosis for these patients remains poor, with a median overall survival <12 months. Therefore, novel treatment options are needed. Currently, there is no brachytherapy device approved for pancreatic cancer treatment. Hereby, we present the protocol of a prospective, multicenter, interventional, open-label, single-arm pilot study (OncoPac-1, Clinicaltrial.gov-NCT03076216) aiming to determine the safety and efficacy of Phosphorus-32 when implanted directly into pancreatic tumors using EUS guidance, for patients with unresectable LAPC undergoing chemotherapy (gemcitabine ± nab-paclitaxel).

White paper on pancreatic ductal adenocarcinoma from society of abdominal radiology's disease-focused panel for pancreatic ductal adenocarcinoma: Part II, update on imaging techniques and screening of pancreatic cancer in high-risk individuals.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive gastrointestinal malignancy with a poor 5-year survival rate. Its high mortality rate is attributed to its aggressive biology and frequently late presentation. While surgical resection remains the only potentially curative treatment, only 10-20% of patients will present with surgically resectable disease. Over the past several years, development of vascular bypass graft techniques and introduction of neoadjuvant treatment regimens have increased the number of patients who can undergo resection with a curative intent. While the role of conventional imaging in the detection, characterization, and staging of patients with PDAC is well established, its role in monitoring treatment response, particularly following neoadjuvant therapy remains challenging because of the complex anatomic and histological nature of PDAC. Novel morphologic and functional imaging techniques (such as DECT, DW-MRI, and PET/MRI) are being investigated to improve the diagnostic accuracy and the ability to measure response to therapy. There is also a growing interest to detect PDAC and its precursor lesions at an early stage in asymptomatic patients to increase the likelihood of achieving cure. This has led to the development of pancreatic cancer screening programs. This article will review recent updates in imaging techniques and the current status of screening and surveillance of individuals at a high risk of developing PDAC.