We investigated the association between serum MUC5AC (sMUC5AC) levels and patient outcomes in individuals who underwent resection for pancreatic ductal adenocarcinoma (PDA), including those treated with neoadjuvant therapy (NAT) and those who had upfront surgery (UpS) followed by adjuvant therapy.

Serum samples from the Ohio State University biorepository collected from January 2010 to June 2021 were utilized. The human MUC5AC kit (NBP2-76703) was used to perform enzyme-linked immunoassays to measure sMUC5AC levels. Logistic regression, Cox regression models (univariate and multivariate), recurrence prediction, analysis of variance (ANOVA), t-tests, and Wilcoxon tests were used for statistical analysis.

In the NAT cohort (n = 23), elevated sMUC5AC levels were significantly (P < 0.05) associated with pathological treatment response, margin positivity, and residual disease. Among 21 patients who had an R0/R1 resection (R2 resection, n=2), higher sMUC5AC levels were associated with shorter progression-free survival (PFS) (HR: 1.64, P = 0.0006) and overall survival (OS) (HR: 1.6, P = 0.005) on univariate analysis. Multivariate models confirmed sMUC5AC as an independent predictor of PFS and OS alongside pathological differentiation and postoperative therapy. Patients with lower sMUC5AC levels had more favorable pathological characteristics, better treatment responses, and improved survival outcomes. These findings were consistent in the FOLFIRINOX subgroup (n = 17). In the UpS cohort (n = 17), post-resection sMUC5AC levels tend to be associated with PFS (P = 0.07) and OS (P = 0.05). Combining sMUC5AC with Carbohydrate antigen (CA) 19-9 enhanced sensitivity (79%) and specificity (67%) to predict recurrence. Higher sMUC5AC levels were associated with earlier recurrence and poor survival outcomes, highlighting its utility in post-surgery risk stratification. Among patients with pre-treatment data (n = 11), sMUC5AC levels were significantly higher among patients with poorly differentiated tumors.

This study provides compelling evidence for the clinical utility of sMUC5AC as a prognostic biomarker among patients with resected PDA. Future large-scale studies are needed to validate these findings and establish standard thresholds for sMUC5AC integration into clinical practice.

To evaluate the CT and MRI features of pancreatic acinar cell carcinoma (pACC) and their association with patient outcome and survival.

This retrospective single-center study included 49 patients with pathology-proven pancreatic acinar cell carcinoma who underwent diagnostic imaging between August 1998 - September 2019. Two radiologists reviewed CT and MRI features independently. Survival was estimated using the Kaplan-Meier method, and Cox proportional-hazards regression model was used to identify factors associated with survival.

pACC tended to present as a solid (31/49, 63.3%) pancreatic head mass (26/49, 53.1%) with ill-defined margins (32/49, 65.3%) and median maximal diameter of 4.1 cm (IQR, 2.9-6.2). Majority of lesions were hypo- or isodense (38/49, 77.6%) compared to normal pancreatic parenchyma, with heterogenous (39/49, 79.6%) enhancement pattern. Biliary ductal dilatation was uncommon, with pancreatic ductal dilatation in 22.4% (11/49) and common bile duct dilatation in 14.3% (7/49). Intralesional calcifications were seen in 6.1% (3/49). Metastasis was present in 71.4% (35/49) of patients at the time of diagnosis. On MRI, 88.9% (16/18) demonstrated diffusion restriction and 59.1% (13/22) with heterogenous enhancement. On multivariate analysis, the imaging presence of T1 hyperintensity (p = 0.02), hypoattenuating necrotic components (p = 0.02), and splenic vein invasion (p = 0.04) were associated with worse survival.

Pancreatic acinar cell carcinoma is a rare pancreatic neoplasm that often presents as a large ill-defined heterogeneously enhancing mass without biliary ductal dilation. T1 hyperintensity, presence of hypoattenuating necrotic components, and splenic vein invasion were independent predictors of survival.

Pancreatic ductal adenocarcinoma (PDAC) with cystic features presents significant challenges in achieving an accurate preoperative diagnosis and in implementing appropriate clinical management. The aim of this study was to analyze the dynamic contrast-enhanced computed tomography (DCE-CT) findings of PDACs with cystic lesions and correlate them with histopathological findings.

We retrospectively reviewed 40 patients with pathology-proven PDACs exhibiting cystic lesions who underwent preoperative DCE-CT imaging. The CT manifestations were classified into three subtypes based on the morphological characteristics of the cystic lesions: Type 1, small proportion (< 50%) of intratumoral cystic lesions, with or without associated peritumoral cystic lesions; Type 2, large proportion (≥ 50%) of intratumoral cystic lesions, with or without associated peritumoral cystic lesions; Type 3, a solid pancreatic mass with accompanying peritumoral cystic lesions. The DCE-CT findings were analyzed based on location, size, contour, enhancement patterns, and secondary findings, and compared with the corresponding pathological diagnoses.

Among the 40 patients, 23 (57.5%) tumors were located in the pancreatic body or tail. Type 1 was identified in 21 cases, Type 2 in 6 cases, and Type 3 in 13 cases. All masses exhibited a bulging pancreatic contour, with 4 cases showing isoattenuating enhancement on DCE-CT. Secondary signs were present in 87.5% (35/40) of cases. Notably, 15 cases (37.5%) were misdiagnosed or missed. Surgical resection specimens demonstrated common pathological features, including large duct-like cysts and coagulative necrosis.

Atypical PDAC with cystic lesions is a relatively uncommon variant that exhibits a range of DCE-CT features, along with distinct pathological characteristics. Familiarity with these imaging features is essential for radiologists in order to minimize the risk of misdiagnosis and guide appropriate clinical management of these challenging cases.

Pancreatic surgery is considered one of the most challenging interventions by many surgeons, mainly due to retroperitoneal location and proximity to key and delicate vascular structures. These factors make pancreatic resection a demanding procedure, with successful rates far from optimal and frequent postoperative complications. Surgical planning is essential to improve patient outcomes, and in this regard, many technological advances made in the last few years have proven to be extremely useful in medical fields. This review aims to outline the potential and limitations of 3D digital and 3D printed models in pancreatic surgical planning, as well as the impact and challenges of novel technologies such as augmented/virtual reality systems or artificial intelligence to improve medical training and surgical outcomes.

Pancreatic cancer, often diagnosed at advanced stages, has poor survival rates. Effective screening aims to detect the disease early, improving outcomes. Current guidelines recommend screening high-risk groups, including those with a family history or genetic predispositions, using methods like endoscopic ultrasound and MRI. The American Gastroenterological Association and other organizations advise annual surveillance for high-risk individuals, typically starting at the age of 50 or 10 years younger than the youngest affected relative. For certain genetic syndromes, such as Peutz-Jeghers syndrome or hereditary pancreatitis, screening may begin as early as the age of 35 to 40 years.

As the pancreas is a low contrast visibility organ, pancreatic ductal adenocarcinoma detection is challenging due to subtle attenuation differences between tumor and pancreatic parenchyma. Photon counting CT (PCCT) has superior iodine contrast-to-noise ratio than conventional CT and also affords the creation of low keV virtual monoenergetic images, both of which increase adenocarcinoma conspicuity. The purpose therefore was to identify the optimal virtual monoenergy for visualizing PDAC during the pancreatic parenchymal phase of enhancement at PCCT using both quantitative and qualitative analyses.

Consecutive patients with pancreatic parenchymal phase PCCT source data were retrospectively identified by PACS search. For the quantitative analysis, region of interest (ROI) measurements were drawn in the pancreatic head, body, tail, pancreatic adenocarcinoma (if present), and psoas muscles on 40-120 keV virtual monoenergetic images in 10 keV increments. Based on the quantitative analysis results and vendor recommendations, four virtual monoenergies(40 keV, 55 keV, 70 keV, and 85 keV) were selected for additional qualitative analysis. Three radiologists blinded to four virtual monoenergies assessed overall image quality, image noise, pancreatic enhancement, and pancreatic mass conspicuity on 5-point Likert scales.

54 patients (28/54 male, mean[SD] age: 62 [13] years) were included. Quantitatively, 40 keV had the highest pancreatic parenchymal CNR and attenuation difference between the adenocarcinoma and parenchyma, but also the highest noise (HUsd). Qualitatively, 70 keV had the best overall image quality (Mean [SE]: 3.7[0.1]) and lower noise than 40 and 55 keV (3.6[0.08] vs. 1.8[0.07] and 2.7[0.05], respectively, p < .001). 40 keV had the greatest pancreatic enhancement (mean[SE] 4.6[0.11]). Adenocarcinoma conspicuity ratings were greatest at 40 keV and 55 keV, and not significantly different from each other (mean[SE] 4.4[0.13] and 4.3[0.14], respectively, Tukey adj-p =.20). 55 keV had greater overall image quality and lower noise than 40 keV (mean[SE] 3.4[0.08] vs. 2.5[0.08], Tukey adj-p < .001 and 2.7[0.05] vs. 1.8[0.07], Tukey adj-p < .001 respectively).

55 keV pancreatic parenchymal phase virtual monoenergetic images afford optimal pancreatic assessment at PCCT for the visualization of pancreatic adenocarcinoma. Routinely viewing 55 keV virtual monoenergetic images at PCCT may improve PDAC detection.

Pancreatic cancer kills millions of people worldwide each year and is one of the most prevalent causes of mortality that requires prompt therapy. A large number of people suffering from pancreatic cancer are detected at an advanced stage, with incurable and drug-resistant tumor, hence the overall survival rate of pancreatic cancer is less. The advance phase of this cancer is generated because of expression of the cancer-causing gene, inactivation of the tumorsuppressing gene, and deregulation of molecules in different cellular signalling pathways. The prompt diagnosis through the biomarkers significantly evades the progress and accelerates the survival rates. The overexpression of Mesothelin, Urokinase plasminogen activator, IGFR, Epidermal growth factor receptor, Plectin-1, Mucin-1 and Zinc transporter 4 were recognized in the diagnosis of pancreatic cancer. Nanotechnology has led to the development of nanocarriersbased formulations (lipid, polymer, inorganic, carbon based and advanced nanocarriers) which overcome the hurdles of conventional therapy, chemotherapy and radiotherapy which causes toxicity to adjacent healthy tissues. The biocompatibility, toxicity and large-scale manufacturing are the hurdles associated with the nanocarriers-based approaches. Currently, Immunotherapybased techniques emerged as an efficient therapeutic alternative for the prevention of cancer. Immunological checkpoint targeting techniques have demonstrated significant efficacy in human cancers. Recent advancements in checkpoint inhibitors, adoptive T cell therapies, and cancer vaccines have shown potential in overcoming the immune evasion mechanisms of pancreatic cancer cells. Combining these immunotherapeutic approaches with nanocarriers holds great promise in enhancing the antitumor response and improving patient survival.

Pancreatic Ductal Adenocarcinoma (PDAC) is an exceptionally deadly form of pancreatic cancer with an extremely low survival rate. From diagnosis to treatment, PDAC is highly challenging to manage. Studies have demonstrated that PDAC tumors in distinct regions of the pancreas exhibit unique characteristics, influencing symptoms, treatment responses, and survival rates. Gaining insight into the heterogeneity of PDAC tumors based on their location in the pancreas can significantly enhance overall management of PDAC. Previous studies have explored PDAC tumor heterogeneity across pancreatic subregions based on their genetic and molecular profiles through biopsy-based histologic assessment. However, biopsy examinations are highly invasive and impractical for large populations. Abdominal imaging, such as Computed Tomography (CT) offers a completely non-invasive means to evaluate PDAC tumor heterogeneity across pancreatic subregions and an opportunity to correlate image feature of tumors with treatment outcome and monitoring. In this study, we explored the inter-tumor heterogeneity in PDAC tumors across three primary pancreatic subregions: the head, body, and tail. Utilizing contrast-enhanced abdominal CT scans and a thorough radiomic analysis of PDAC tumors, several morphological and textural tumor features were identified to be notably different between tumors in the head and those in the body and tail regions. To validate the significance of the identified features, a machine learning ML model was trained to automatically classify PDAC tumors into their respective regions i.e. head or body/tail subregion using their CT features. The study involved 200 CT abdominal scans, with 100 used for radiomic analysis and model training, and the remaining 100 for model testing. The ML model achieved an average classification accuracy, sensitivity, and specificity of 87%, 86%, and 88% on the testing scans respectively. Evaluating the heterogeneity of PDAC tumors across pancreatic subregions provides valuable insights into tumor composition and has the potential to enhance diagnosis and personalize treatment based on tumor characteristics and location.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis and rising global deaths. Late diagnosis, due to absent early symptoms and biomarkers, limits treatment mainly to chemotherapy, which soon encounters resistance. PDAC treatment innovation is hampered by its complex and heterogeneous resistant nature, including mutations in key genes and a stromal-rich, immunosuppressive tumour microenvironment. Recent studies on PDAC resistance stress the need for suitable in vitro and ex vivo models to replicate its complex molecular and microenvironmental landscape. This review summarises advances in these models, which can aid in combating chemoresistance and serve as platforms for discovering new therapeutics. Immortalised cell lines offer homogeneity, unlimited proliferation, and reproducibility, but while many gemcitabine-resistant PDAC cell lines exist, fewer models are available for resistance to other drugs. Organoids from PDAC patients show promise in mimicking tumour heterogeneity and chemosensitivity. Bioreactors, co-culture systems and organotypic slices, incorporating stromal and immune cells, are being developed to understand tumour-stroma interactions and the tumour microenvironment's role in drug resistance. Lastly, another innovative approach is three-dimensional bioprinting, which creates tissue-like structures resembling PDAC architecture, allowing for drug screening. These advanced models can guide researchers in selecting optimal in vitro tests, potentially improving therapeutic strategies and patient outcomes.

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.

Low contrast resolution in abdominal computed tomography (CT) may be negatively affected by attempts to lower patient doses. Iterative reconstruction (IR) algorithms play a key role in mitigating this problem. The reconstructed slice thickness also influences image quality. The aim was to assess the interaction and influence of patient dose, slice thickness, and IR strength on image quality in abdominal CT.

With a simultaneous acquisition, images at 42 and 98 mAs were obtained in 25 patients. Multiplanar images with slice thicknesses of 1, 2, and 3 mm and advanced modeled iterative reconstruction (ADMIRE) strengths of 3 (AD3) and 5 (AD5) were reconstructed. Four radiologists evaluated the images in a pairwise manner based on five image criteria. Ordinal logistic regression with mixed effects was used to evaluate the effect of tube load, ADMIRE strength, and slice thickness using the visual grading regression technique.

For all assessed image criteria, the regression analysis showed significantly (p < 0.001) higher image quality for AD5, but lower for tube load 42 mAs, and slice thicknesses of 1 mm and 2 mm, compared to the reference categories of AD3, 98 mAs, and 3 mm, respectively. AD5 at 2 mm was superior to AD3 at 3 mm for all image criteria studied. AD5 1 mm produced inferior image quality for liver parenchyma and overall image quality compared to AD3 3 mm. Interobserver agreement (ICC) ranged from 0.874 to 0.920.

ADMIRE 5 at 2 mm slice thickness may allow for further dose reductions due to its superiority when compared to ADMIRE 3 at 3 mm slice thickness.

Combination of thinner slices and higher ADMIRE strength facilitates imaging at low dose.

Neoadjuvant therapy (NAT) for early-stage pancreatic ductal adenocarcinoma (PDA) has recently gained prominence. We investigated the clinical significance of mucin 5 AC (MUC5AC), which exists in two major glycoforms, a less-glycosylated immature isoform (IM) and a heavily glycosylated mature isoform (MM), as a biomarker in resected PDA. Immunohistochemistry was performed on 100 resected PDAs to evaluate the expression of the IM and MM of MUC5AC using their respective monoclonal antibodies, CLH2 (NBP2-44455) and 45M1 (ab3649). MUC5AC localization (cytoplasmic, apical, and extra-cellular (EC)) was determined, and the H-scores were calculated. Univariate and multivariate (MVA) Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). Of 100 resected PDA patients, 43 received NAT, and 57 were treatment-naïve with upfront surgery (UpS). In the study population (n = 100), IM expression (H-scores for objective response vs. no response vs. UpS = 104 vs. 152 vs. 163, p = 0.01) and MM-MUC5AC detection rates (56% vs. 63% vs. 82%, p = 0.02) were significantly different. In the NAT group, MM-MUC5AC-negative patients had significantly better PFS according to the MVA (Hazard Ratio: 0.2, 95% CI: 0.059-0.766, p = 0.01). Similar results were noted in a FOLFIRINOX sub-group (n = 36). We established an association of MUC5AC expression with treatment response and outcomes.

To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer.

This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists.

The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88.

The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.

Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors.

We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients).

64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352).

Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.

One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were used to evaluate the predictive performance of the scoring system.

Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).

ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.

The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.

Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas's deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest cancers, presents significant challenges in diagnosis and treatment due to its aggressive, metastatic nature and lack of early detection methods. A key obstacle in PDAC treatment is the highly complex tumor environment characterized by dense stroma surrounding the tumor, which hinders effective drug delivery. Nanotechnology can offer innovative solutions to these challenges, particularly in creating novel drug delivery systems for existing anticancer drugs for PDAC, such as gemcitabine and paclitaxel. By using customization methods such as incorporating conjugated targeting ligands, tumor-penetrating peptides, and therapeutic nucleic acids, these nanoparticle-based systems enhance drug solubility, extend circulation time, improve tumor targeting, and control drug release, thereby minimizing side effects and toxicity in healthy tissues. Moreover, nanoparticles have also shown potential in precise diagnostic methods for PDAC. This literature review will delve into targeted mechanisms, pathways, and approaches in treating pancreatic cancer. Additional emphasis is placed on the study of nanoparticle-based delivery systems, with a brief mention of those in clinical trials. Overall, the overview illustrates the significant advances in nanomedicine, underscoring its role in transcending the constraints of conventional PDAC therapies and diagnostics.

Given the limitation of current routine approaches for pancreatic cancer screening and detection, the mortality rate of pancreatic cancer cases is still critical. The development of blood-based molecular biomarkers for pancreatic cancer screening and early detection which provide less-invasive, high-sensitivity, and cost-effective, is urgently needed. The goal of this study is to identify and validate the potential molecular biomarkers in white blood cells (WBCs) of pancreatic cancer patients. Gene expression profiles of pancreatic cancer patients from NCBI GEO database were analyzed by CU-DREAM. Then, mRNA expression levels of three candidate genes were determined by quantitative RT-PCR in WBCs of pancreatic cancer patients (N = 27) and healthy controls (N = 51). ROC analysis was performed to assess the performance of each candidate gene. A total of 29 upregulated genes were identified and three selected genes were performed gene expression analysis. Our results revealed high mRNA expression levels in WBCs of pancreatic cancer patients in all selected genes, including FKBP1A (p < 0.0001), PLD1 (p < 0.0001), and PSMA4 (p = 0.0002). Among candidate genes, FKBP1A mRNA expression level was remarkably increased in the pancreatic cancer samples and also in the early stage (p < 0.0001). Moreover, FKBP1A showed the greatest performance to discriminate patients with pancreatic cancer from healthy individuals than other genes with the 88.9% sensitivity, 84.3% specificity, and 90.1% accuracy. Our findings demonstrated that the alteration of FKBP1A gene in WBCs serves as a novel valuable biomarker for patients with pancreatic cancer. Detection of FKBP1A mRNA expression level in circulating WBCs, providing high-sensitive, less-invasive, and cost-effective, is simple and feasible for routine clinical setting that can be applied for pancreatic cancer screening and early detection.

The exocrine part of the pancreas has a duct system called the pancreatic ductal system (PDS). Its mechanism of development is complex, and any reorganization during early embryogenesis can give rise to anatomical variants. The aim of this study is to collect, classify, and analyze published evidence on the importance of anatomical variants of the PDS, addressing gaps in our understanding of such variations. The MEDLINE, Web of Science, Embase, and Google Scholar databases were searched to identify publications relevant to this review. R studio with meta-package was used for data extraction, risk of bias estimation, and statistical analysis. A total of 64 studies out of 1,778 proved suitable for this review and metanalysis. The meta-analysis computed the prevalence of normal variants of the PDS (92% of 10,514 subjects). Type 3 variants and "descending" subtypes of the main pancreatic duct (MPD) predominated in the pooled samples. The mean lengths of the MPD and accessory pancreatic duct (APD) were 16.53 cm and 3.36 cm, respectively. The mean diameters of the MPD at the head and the APD were 3.43 mm and 1.69 mm, respectively. The APD was present in only 41% of samples, and the long type predominated. The pancreatic ductal anatomy is highly variable, and the incorrect identification of variants may be challenging for surgeons during ductal anastomosis with gut, failure to which may often cause ductal obstruction or pseudocysts formation.

Serous microcystic adenoma (SMA), a primary benign pancreatic tumor which can be clinically followed-up instead of undergoing surgery, are sometimes mis-distinguished as pancreatic neuroendocrine tumor (pNET) in regular preoperative imaging examinations. This study aimed to analyze preoperative contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) features of SMAs in comparison to pNETs.

In this retrospective study, patients with imaging-diagnosed pancreatic lesions were screened between October 2020 to October 2022 (ethical approval No. B2020-309R). Performing by a Siemens Sequoia (Siemens Medical Solutions, Mountain View, CA, USA) equipped with a 5C-1 curved array transducer (3.0-4.5 MHz), CEUS examination was conducted to observe the microvascular perfusion patterns of pancreatic lesions in arterial phase, venous/late phases (VLP) using SonoVue® (Bracco Imaging Spa, Milan, Italy) as the contrast agent. Virtual touch tissue imaging and quantification (VTIQ) - SWE was used to measure the shear wave velocity (SWV, m/s) value to represent the quantitative stiffness of pancreatic lesions. Multivariate logistic regression was performed to analyze potential ultrasound and clinical features in discriminating SMAs and pNETs.

Finally, 30 SMA and 40 pNET patients were included. All pancreatic lesions were pathologically proven via biopsy or surgery. During the arterial phase of CEUS, most SMAs and pNETs showed iso- or hyperenhancement (29/30, 97 % and 31/40, 78 %), with a specific early honeycomb enhancement pattern appeared in 14/30 (47 %) SMA lesions. During the VLP, while most of the SMA lesions remained iso- or hyperenhancement (25/30, 83 %), nearly half of the pNET lesions revealed an attenuated hypoenhancement (17/40, 43 %). The proportion of hypoenhancement pattern during the VLP of CEUS differed significantly between SMAs and pNETs (P = 0.021). The measured SWV value of SMAs was significantly higher than pNETs (2.04 ± 0.70 m/s versus 1.42 ± 0.44 m/s, P = 0.002). Taking a SWV value > 1.83 m/s as a cutoff in differentiating SMAs and pNETs, the area under the receiver operating characteristic curve (AUROC) was 0.825, with sensitivity, specificity and likelihood ratio (+) of 85.71 %, 72.73 % and 3.143, respectively. Multivariate logistic regression revealed that SWV value (m/s) of the pancreatic lesion was an independent variable in discriminating SMA and pNET.

By comprehensively evaluating CEUS patterns and SWE features, SMA and pNET may be well differentiated before the operation. While SMA typically presents as harder lesion in VTIQ-SWE, exhibiting a specific honeycomb hyperenhancement pattern during the arterial phase of CEUS, pNET is characterized by relative softness, occasionally displaying a wash-out pattern during the VLP of CEUS.

The diagnosis and monitoring of disease through the detection of circulating protein biomarkers is a growing field in the practice of oncology. The search for more effective protein biomarkers to aid in the diagnosis and treatment of patients with pancreatic ductal adenocarcinoma (PDAC) remains a valuable area of study, given the aggressive and often occult nature of this malignancy. Liquid biopsies are attractive, as they offer a minimally invasive and cost-effective approach when compared to traditional biopsy methods and imaging modalities used for diagnosis and surveillance. Carbohydrate antigen (CA) 19-9 is currently the most commonly used serum protein biomarker for the diagnosis and monitoring of patients with PDAC, but due to its sensitivity and specificity, its utility remains limited. In this review, we examine how circulating protein biomarkers are used in the diagnosis, prognostication, and surveillance of PDAC. We also highlight protein biomarkers that are currently under investigation that have the potential to enhance our ability to detect early-stage malignancies, predict response to therapy, and monitor for recurrence, but these markers require larger prospective validation studies before they can be widely implemented. Continued efforts to identify and validate novel biomarkers will be crucial for improving the management and outcomes of patients with this challenging disease.

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and treatment-refractory malignancies. The lack of an effective screening tool results in the majority of patients being diagnosed at late stages, which underscores the urgent need to develop more sensitive and specific imaging modalities, particularly in detecting occult metastases, to aid clinical decision-making. The tumor microenvironment of PDAC is heavily infiltrated with myeloid-derived suppressor cells (MDSCs) that express C-C chemokine receptor type 2 (CCR2). These CCR2-expressing MDSCs accumulate at a very early stage of metastasis and greatly outnumber PDAC cells, making CCR2 a promising target for detecting early, small metastatic lesions that have scant PDAC cells. Herein, we evaluated a CCR2 targeting PET tracer (68Ga-DOTA-ECL1i) for PET imaging on PDAC metastasis in two mouse models. Positron emission tomography/computed tomography (PET/CT) imaging of 68Ga-DOTA-ECL1i was performed in a hemisplenic injection metastasis model (KI) and a genetically engineered orthotopic PDAC model (KPC), which were compared with 18F-FDG PET concurrently. Autoradiography, hematoxylin and eosin (H&E), and CCR2 immunohistochemical staining were performed to characterize the metastatic lesions. PET/CT images visualized the PDAC metastases in the liver/lung of KI mice and in the liver of KPC mice. Quantitative uptake analysis revealed increased metastasis uptake during disease progression in both models. In comparison, 18F-FDG PET failed to detect any metastases during the time course studies. H&E staining showed metastases in the liver and lung of KI mice, within which immunostaining clearly demonstrated the overexpression of CCR2 as well as CCR2+ cell infiltration into the normal liver. H&E staining, CCR2 staining, and autoradiography also confirmed the expression of CCR2 and the uptake of 68Ga-DOTA-ECL1i in the metastatic foci in KPC mice. Using our novel CCR2 targeted radiotracer 68Ga-DOTA-ECL1i and PET/CT, we demonstrated the sensitive and specific detection of CCR2 in the early PDAC metastases in two mouse models, indicating its potential in future clinical translation.

To assess the resectability of pancreatic ductal adenocarcinoma (PDAC), the evaluation of tumor vascular contact holds paramount significance. This study aimed to compare the image quality and diagnostic performance of high-resolution (HR) pancreas computed tomography (CT) using an 80 kVp tube voltage and a thin slice (1 mm) for assessing PDAC resectability, in comparison with the standard protocol CT using 120 kVp.

This research constitutes a secondary analysis originating from a multicenter prospective study. All participants underwent both the standard protocol pancreas CT using 120 kVp with 3 mm slice thickness (ST) and HR-CT utilizing an 80 kVp tube voltage and 1 mm ST. The contrast-to-noise ratio (CNR) between parenchyma and tumor, along with the degree of enhancement of the abdominal aorta and main portal vein (MPV), were measured and subsequently compared. Additionally, the likelihood of margin-negative resection (R0) was evaluated using a five-point scale. The diagnostic performance of both CT protocols in predicting R0 resection was assessed through the area under the receiver operating characteristic curve (AUC).

A total of 69 patients (37 males and 32 females; median age, 66.5 years) were included in the study. The median CNR of PDAC was 10.4 in HR-CT, which was significantly higher than the 7.1 in the standard CT (P=0.006). Furthermore, HR-CT demonstrated notably higher median attenuation values for both the abdominal aorta (579.5 HU vs. 327.2 HU; P=0.002) and the MPV (263.0 HU vs. 175.6 HU; P=0.004) in comparison with standard CT. Following surgery, R0 resection was achieved in 51 patients. The pooled AUC for HR-CT in predicting R0 resection was 0.727, slightly exceeding the 0.699 of standard CT, albeit lacking a significant statistical distinction (P=0.128).

While HR pancreas CT using 80 kVp offered a notably greater degree of contrast enhancement in vessels and a higher CNR for PDAC compared to standard CT, its diagnostic performance in predicting R0 resection remained statistically comparable.

Glypican-1 (GPC1) is overexpressed in several solid cancers and is associated with tumor progression, whereas its expression is low in normal tissues. This study aimed to evaluate the potential of an anti-GPC1 monoclonal antibody (GPC1 mAb) labeled with 89Zr or 211At as a theranostic target in pancreatic ductal adenocarcinoma. Methods: GPC1 mAb clone 01a033 was labeled with 89Zr or 211At with a deferoxamine or decaborane linker, respectively. The internalization ability of GPC1 mAb was evaluated by fluorescence conjugation using a confocal microscope. PANC-1 xenograft mice (n = 6) were intravenously administered [89Zr]GPC1 mAb (0.91 ± 0.10 MBq), and PET/CT scanning was performed for 7 d. Uptake specificity was confirmed through a comparative study using GPC1-positive (BxPC-3) and GPC1-negative (BxPC-3 GPC1-knockout) xenografts (each n = 3) and a blocking study. DNA double-strand breaks were evaluated using the γH2AX antibody. The antitumor effect was evaluated by administering [211At]GPC1 mAb (∼100 kBq) to PANC-1 xenograft mice (n = 10). Results: GPC1 mAb clone 01a033 showed increased internalization ratios over time. One day after administration, a high accumulation of [89Zr]GPC1 mAb was observed in the PANC-1 xenograft (SUVmax, 3.85 ± 0.10), which gradually decreased until day 7 (SUVmax, 2.16 ± 0.30). The uptake in the BxPC-3 xenograft was significantly higher than in the BxPC-3 GPC1-knockout xenograft (SUVmax, 4.66 ± 0.40 and 2.36 ± 0.36, respectively; P = 0.05). The uptake was significantly inhibited in the blocking group compared with the nonblocking group (percentage injected dose per gram, 7.3 ± 1.3 and 12.4 ± 3.0, respectively; P = 0.05). DNA double-strand breaks were observed by adding 150 kBq of [211At]GPC1 and were significantly suppressed by the internalization inhibitor (dynasore), suggesting a substantial contribution of the internalization ability to the antitumor effect. Tumor growth suppression was observed in PANC-1 mice after the administration of [211At]GPC1 mAb. Internalization inhibitors (prochlorperazine) significantly inhibited the therapeutic effect of [211At]GPC1 mAb, suggesting an essential role in targeted α-therapy. Conclusion: [89Zr]GPC1 mAb PET showed high tumoral uptake in the early phase after administration, and targeted α-therapy using [211At]GPC1 mAb showed tumor growth suppression. GPC1 is a promising target for future applications for the precise diagnosis of pancreatic ductal adenocarcinoma and GPC1-targeted theranostics.

This study proposes and evaluates a deep learning method to detect pancreatic neoplasms and to identify main pancreatic duct (MPD) dilatation on portal venous computed tomography scans.

A total of 2890 portal venous computed tomography scans from 9 institutions were acquired, among which 2185 had a pancreatic neoplasm and 705 were healthy controls. Each scan was reviewed by one in a group of 9 radiologists. Physicians contoured the pancreas, pancreatic lesions if present, and the MPD if visible. They also assessed tumor type and MPD dilatation. Data were split into a training and independent testing set of 2134 and 756 cases, respectively.A method to detect pancreatic lesions and MPD dilatation was built in 3 steps. First, a segmentation network was trained in a 5-fold cross-validation manner. Second, outputs of this network were postprocessed to extract imaging features: a normalized lesion risk, the predicted lesion diameter, and the MPD diameter in the head, body, and tail of the pancreas. Third, 2 logistic regression models were calibrated to predict lesion presence and MPD dilatation, respectively. Performance was assessed on the independent test cohort using receiver operating characteristic analysis. The method was also evaluated on subgroups defined based on lesion types and characteristics.

The area under the curve of the model detecting lesion presence in a patient was 0.98 (95% confidence interval [CI], 0.97-0.99). A sensitivity of 0.94 (469 of 493; 95% CI, 0.92-0.97) was reported. Similar values were obtained in patients with small (less than 2 cm) and isodense lesions with a sensitivity of 0.94 (115 of 123; 95% CI, 0.87-0.98) and 0.95 (53 of 56, 95% CI, 0.87-1.0), respectively. The model sensitivity was also comparable across lesion types with values of 0.94 (95% CI, 0.91-0.97), 1.0 (95% CI, 0.98-1.0), 0.96 (95% CI, 0.97-1.0) for pancreatic ductal adenocarcinoma, neuroendocrine tumor, and intraductal papillary neoplasm, respectively. Regarding MPD dilatation detection, the model had an area under the curve of 0.97 (95% CI, 0.96-0.98).

The proposed approach showed high quantitative performance to identify patients with pancreatic neoplasms and to detect MPD dilatation on an independent test cohort. Performance was robust across subgroups of patients with different lesion characteristics and types. Results confirmed the interest to combine a direct lesion detection approach with secondary features such as the MPD diameter, thus indicating a promising avenue for the detection of pancreatic cancer at early stages.

Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery.

Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents.

After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT.

The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning.

The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students.

• A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent pancreatic neoplasm accounting for more than 90% of pancreatic malignancies. Surgical resection with adequate lymphadenectomy remains the only available curative strategy for patients with PDAC. Despite improvements in both chemotherapy regimen and surgical care, body/neck PDAC still conveys a poor prognosis because of the vicinity of major vascular structures, including celiac trunk, which favors insidious disease spread at the time of diagnosis. Body/neck PDAC involving the celiac trunk is considered locally advanced PDAC in most guidelines and therefore not eligible for upfront resection. However, a more aggressive surgical approach (i.e., distal pancreatectomy with splenectomy and en-bloc celiac trunk resection [DP-CAR]) was recently proposed to offer hope for cure in selected patients with locally advanced body/neck PDAC responsive to induction therapy at the cost of higher morbidity. The so-called "modified Appleby procedure" is highly demanding and requires optimal preoperative staging as well as appropriate patient preparation for surgery (i.e., preoperative arterial embolization). Herein, we review current evidence regarding DP-CAR indications and outcomes as well as the critical role of diagnostic and interventional radiology in patient preparation before DP-CAR, and early identification and management of DP-CAR complications.

There is a high clinical unmet need to improve outcomes for pancreatic ductal adenocarcinoma (PDAC) patients, either with the discovery of new therapies or biomarkers that can track response to treatment more efficiently than imaging. We report an innovative approach that will generate renewed interest in using circulating tumor cells (CTCs) to monitor treatment efficacy, which, in this case, used PDAC patients receiving an exploratory new therapy, poly ADP-ribose polymerase inhibitor (PARPi)-niraparib-as a case study. CTCs were enumerated from whole blood using a microfluidic approach that affinity captures epithelial and mesenchymal CTCs using anti-EpCAM and anti-FAPα monoclonal antibodies, respectively. These antibodies were poised on the surface of two separate microfluidic devices to discretely capture each subpopulation for interrogation. The isolated CTCs were enumerated using immunophenotyping to produce a numerical ratio consisting of the number of mesenchymal to epithelial CTCs (denoted "Φ"), which was used as an indicator of response to therapy, as determined using computed tomography (CT). A decreasing value of Φ during treatment was indicative of tumor response to the PARPi and was observed in 88% of the enrolled patients (n = 31). Changes in Φ during longitudinal testing were a better predictor of treatment response than the current standard CA19-9. We were able to differentiate between responders and non-responders using ΔΦ (p = 0.0093) with higher confidence than CA19-9 (p = 0.033). For CA19-9 non-producers, ΔΦ correctly predicted the outcome in 72% of the PDAC patients. Sequencing of the gDNA extracted from affinity-selected CTC subpopulations provided information that could be used for patient enrollment into the clinical trial based on their tumor mutational status in DNA repair genes.

Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.

Accurate staging prior to resection of pancreatic ductal adenocarcinoma (PDAC) is imperative to avoid unnecessary operative morbidity and oncologic futility in patients with occult intra-abdominal distant metastases. We aimed to determine the diagnostic yield of staging laparoscopy (SL) and to identify factors associated with increased risk of positive laparoscopy (PL) in the modern era.

Patients with radiographically localized PDAC who underwent SL from 2017 to 2021 were retrospectively reviewed. The yield of SL was defined as the proportion of patients with PL, including gross metastases and/or positive peritoneal cytology. Factors associated with PL were assessed using univariate analysis and multivariable logistic regression.

Of 1,004 patients who underwent SL, 180 (18%) had PL due to gross metastases (n = 140) and/or positive cytology (n = 96). Patients who had neoadjuvant chemotherapy prior to laparoscopy had lower rates of PL (14% vs 22%, p = 0.002). When the analysis was restricted to chemo-naive patients who had concurrent peritoneal lavage performed, 95 of 419 patients (23%) had PL. In multivariable analysis, PL was associated with younger (<60) age, indeterminate extrapancreatic lesions on preoperative imaging, body/tail tumor location, larger tumor size, and elevated serum CA 19-9 (all p < 0.05). Among patients with no indeterminate extrapancreatic lesions on preoperative imaging, the rate of PL ranged from 1.6% in patients with no risk factors to 42% in young patients with large body/tail tumors and elevated serum CA 19-9.

The rate of PL in patients with PDAC remains high in the modern era. SL with peritoneal lavage should be considered for the majority of patients prior to resection, specifically those with high-risk features, and ideally prior to neoadjuvant chemotherapy.

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Chronic pancreatitis (CP) is a known risk factor for pancreatic cancer. CP may present with an inflammatory mass, and differentiation from pancreatic cancer is often difficult. Clinical suspicion of malignancy dictates a need for further evaluation for underlying pancreatic cancer. Imaging modalities remain the mainstay of evaluation for a mass in background CP; however, they have their shortcomings. Endoscopic ultrasound (EUS) has become the go-to investigation. Adjunct modalities such as contrast-harmonic EUS and EUS elastography, as well as EUS-guided sampling using newer-generation needles are useful in differentiating inflammatory from malignant masses in the pancreas. Paraduodenal pancreatitis and autoimmune pancreatitis often masquerade as pancreatic cancer. In this narrative review, we discuss the various modalities used to differentiate inflammatory from malignant masses of the pancreas.