Mass-forming pancreatitis versus pancreatic ductal adenocarcinoma: CT and MR imaging for differentiation

Proliferative features of IgG4-related disease

IgG4-related disease is an immune-mediated disease that can lead to substantial morbidity and organ damage. Capable of affecting nearly any organ system or anatomic site, and showing considerable overlap in clinical presentation with various other diseases, IgG4-related disease often poses a diagnostic challenge for clinicians. Furthermore, there are no diagnostic biomarkers with high specificity for IgG4-related disease, and histopathological examination is nuanced and requires clinical correlation for accurate diagnosis. Therefore, it is crucial for clinicians to recognise the clinical phenotypes of IgG4-related disease. The disease is generally considered to have predominantly fibrotic and proliferative (or inflammatory) manifestations, with distinct clinical, serological and histopathological findings associated with each manifestation. However, the fibrotic and proliferative manifestations of this disease frequently occur together, thereby blurring this dichotomous distinction. In this Series paper, we provide a detailed overview of the clinical manifestations typical of the proliferative features of IgG4-related disease, with an emphasis on the diagnostic evaluation and differential diagnosis of each proliferative disease manifestation. In addition, we summarise the immune mechanisms underlying IgG4-related disease, suggest a framework for how to approach management and monitoring after the diagnosis is established, and highlight current unmet needs for patient care surrounding this disease.

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis

OBJECTIVES

To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis.

METHODS

This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 and September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset [N = 291; N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)]. A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using dice similarity coefficient (DSC) and intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumour and oedema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix.

RESULTS

Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve (AUC) in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior.

CONCLUSION

MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions.

ADVANCES IN KNOWLEDGE

This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.

Evaluation of the diagnostic utility of NCOA3, Maspin and VHL protein expression in pancreatic ductal adenocarcinoma: An immunohistochemical study

Pancreatic ductal adenocarcinoma (PDAC) is a lethal tumor with a high mortality rate. The distinction between PDAC and chronic pancreatitis is sometimes challenging on routine histopathological examination, highlighting the need to identify biomarkers that can facilitate this distinction. This retrospective study was conducted to evaluate the diagnostic utility of nuclear receptor co-activator 3 (NCOA3), Maspin and Von Hippel-Lindau protein (VHL) immunostaining in PDAC. Eighty cases of PDAC, 46 cases of chronic pancreatitis and 53 normal pancreatic tissue were immunohistochemically assessed using NCOA3, Maspin and VHL antibodies on sections from a tissue microarray. NCOA3, Maspin and VHL were positive in 90 %, 100 % and 35 %, of PDAC cases respectively, whereas NCOA3, Maspin and VHL expressions were positive in 3.8 %, 0 and 100 % of normal pancreatic tissue and in 15.2 %, 21.7 % and 97.8 % of chronic pancreatitis cases respectively. Significant differences were observed between PDAC and other groups regarding NCOA3, Maspin and VHL expression (p < 0.001). The H scores of NCOA3, Maspin and VHL could significantly distinguish between PDAC and normal cases with high sensitivity (90 %, 100 % and 98.75 % respectively) and specificity (100 %, 100 % and 96.23 % respectively). Similar findings were found in the distinction between PDAC and chronic pancreatitis (Sensitivity: 90 %, 95.25 % and 98.75 %; specificity: 100 %, 100 % and 93.48 % for NCOA3, Maspin and VHL respectively). In conclusion, NCOA3 and Maspin were found to be significantly expressed in PDAC compared to non-tumorous tissue while VHL was significantly expressed in non-tumorous tissue. A panel of NCOA3, Maspin and VHL could potentially distinguish PDAC from non-tumorous pancreatic tissue.

A Comparison of Preoperative Predictive Scoring Systems for Postoperative Pancreatic Fistula after Pancreaticoduodenectomy Based on a Single-Center Analysis

Background: Postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) is associated with major postoperative morbidity and mortality. Several scoring systems have been described to stratify patients into risk groups according to the risk of POPF. The aim of this study was to compare scoring systems in patients who underwent a PD. Methods: A total of 196 patients undergoing PD from July 2019 to June 2022 were identified from a prospectively maintained database of the University Hospital Ghent. After performing a literature search, four validated, solely preoperative risk scores and the intraoperative Fistula Risk Score (FRS) were included in our analysis. Furthermore, we eliminated the variable blood loss (BL) from the FRS and created an additional score. Univariate and multivariate analyses were performed for all risk factors, followed by a ROC analysis for the six scoring systems. Results: All scores showed strong prognostic stratification for developing POPF (p < 0.001). FRS showed the best predictive accuracy in general (AUC 0.862). FRS without BL presented the best prognostic value of the scores that included solely preoperative variables (AUC 0.783). Soft pancreatic texture, male gender, and diameter of the Wirsung duct were independent prognostic factors on multivariate analysis. Conclusions: Although all predictive scoring systems stratify patients accurately by risk of POPF, preoperative risk stratification could improve clinical decision-making and implement preventive strategies for high-risk patients. Therefore, the preoperative use of the FRS without BL is a potential alternative.

Quantitative MR imaging biomarkers for distinguishing inflammatory pancreatic mass and pancreatic cancer-a systematic review and meta-analysis

OBJECTIVES

To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC).

METHODS

A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity.

RESULTS

Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values.

CONCLUSIONS

Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies.

CLINICAL RELEVANCE STATEMENT

Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery.

KEY POINTS

• Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.

Multiparametric Characterization of the DSL-6A/C1 Pancreatic Cancer Model in Rats

The DSL-6A/C1 murine pancreatic ductal adenocarcinoma (PDAC) tumor model was established in Lewis rats and characterized through a comprehensive multiparametric analysis to compare it to other preclinical tumor models and explore potential diagnostic and therapeutical targets. DSL-6A/C1 tumors were histologically analyzed to elucidate PDAC features. The tumor microenvironment was studied for immune cell prevalence. Multiparametric MRI and PET imaging were utilized to characterize tumors, and 68Ga-FAPI-46-targeting cancer-associated fibroblasts (CAFs), were used to validate the histological findings. The histology confirmed typical PDAC characteristics, such as malformed pancreatic ductal malignant cells and CAFs. Distinct immune landscapes were identified, revealing an increased presence of CD8+ T cells and a decreased CD4+ T cell fraction within the tumor microenvironment. PET imaging with 68Ga-FAPI tracers exhibited strong tracer uptake in tumor tissues. The MRI parameters indicated increasing intralesional necrosis over time and elevated contrast media uptake in vital tumor areas. We have demonstrated that the DSL-6A/C1 tumor model, particularly due to its high tumorigenicity, tumor size, and 68Ga-FAPI-46 sensitivity, is a suitable alternative to established small animal models for many forms of preclinical analyses and therapeutic studies of PDAC.

Chemokine Receptor 2 Targeted PET/CT Imaging Distant Metastases in Pancreatic Ductal Adenocarcinoma

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.

Pancreatic Cancer and Its Mimics

Pancreatic ductal adenocarcinoma (PDAC) is the most common primary pancreatic malignancy, ranking fourth in cancer-related mortality in the United States. Typically, PDAC appears on images as a hypovascular mass with upstream pancreatic duct dilatation and abrupt duct cutoff, distal pancreatic atrophy, and vascular encasement, with metastatic involvement including lymphadenopathy. However, atypical manifestations that may limit detection of the underlying PDAC may also occur. Atypical PDAC features include findings related to associated conditions such as acute or chronic pancreatitis, a mass that is isointense to the parenchyma, multiplicity, diffuse tumor infiltration, associated calcifications, and cystic components. Several neoplastic and inflammatory conditions can mimic PDAC, such as paraduodenal "groove" pancreatitis, autoimmune pancreatitis, focal acute and chronic pancreatitis, neuroendocrine tumors, solid pseudopapillary neoplasms, metastases, and lymphoma. Differentiation of these conditions from PDAC can be challenging due to overlapping CT and MRI features; however, certain findings can help in differentiation. Diffusion-weighted MRI can be helpful but also can be nonspecific. Accurate diagnosis is pivotal for guiding therapeutic planning and potential outcomes in PDAC and avoiding biopsy or surgical treatment of some of these mimics. Biopsy may still be required for diagnosis in some cases. The authors describe the typical and atypical imaging findings of PDAC and features that may help to differentiate PDAC from its mimics. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Zins in this issue.

Clinical Value of Mean Platelet Volume to Platelet Ratio (MPR) in Distinguishing Mass-Forming Chronic Pancreatitis and Pancreatic Cancer

BACKGROUND

Correctly distinguishing mass-forming chronic pancreatitis (MFCP) from pancreatic cancer (PC) is of clinical significance to determine optimal therapy and improve the prognosis of patients. According to research, inflammation status in PC is different from that in MFCP. Mean platelet volume/platelet ratio (MPR) is a platelet-related inflammation index which has been proven to be valuable in the diagnosis and prognosis of various malignant cancers due to the change in mean platelet volume and platelet count under abnormal inflammatory conditions caused by tumors. Thus, we conducted this study to investigate the clinical value of MPR in distinguishing MFCP from PC.

METHODS

We retrospectively analyzed the data of 422 patients who were suspected to have PC during imaging examination at our department from January 2012 to December 2021. Included patients were divided into the PC (n = 383) and MFCP groups (n = 39), according to their pathological diagnosis. Clinical data including MPR were compared within these two groups and the diagnostic value was explored using logistic regression. The ROC curve between MPR and PC occurrence was drawn and an optimal cut-off value was obtained. Propensity score matching was applied to match MFCP patients with PC patients according to their age and carbohydrate antigen 19-9 (CA19-9). Differences in MPR between groups were compared to verify our findings.

RESULTS

The area under the ROC curve between MPR and PC occurrence was 0.728 (95%CI: 0.652-0.805) and the optimal cut-off value was 0.045 with a 69.2% sensitivity and 68.0% accuracy. For all the included patients, MPRs in the MFCP and PC groups were 0.04 (0.04, 0.06) and 0.06 (0.04, 0.07), respectively (p = 0.005). In patients with matching propensity scores, MPRs in the MFCP and PC groups were 0.04 (0.03, 0.06) and 0.06 (0.05, 0.08), respectively (p = 0.005). Multiple logistic regression in all included patients and matched patients confirmed MPR and CA19-9 as independent risk markers in distinguishing PC. Combining CA19-9 with MPR can increase the sensitivity and accuracy in diagnosing PC to 93.2% and 89.5%, respectively.

CONCLUSION

MPR in PC patients is significantly higher than that in MFCP patients and may be adopted as a potential indicator to distinguish MFCP and PC. Its differential diagnosis capacity can be improved if combined with CA19-9.

Contrast-enhanced ultrasound and contrast-enhanced computed tomography for differentiating mass-forming pancreatitis from pancreatic ductal adenocarcinoma: a meta-analysis

BACKGROUND

Patients with mass-forming pancreatitis (MFP) or pancreatic ductal adenocarcinoma (PDAC) presented similar clinical symptoms, but required different treatment approaches and had different survival outcomes. This meta-analysis aimed to compare the diagnostic performance of contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT) in differentiating MFP from PDAC.

METHODS

A literature search was performed in the PubMed, EMBASE (Ovid), Cochrane Library (CENTRAL), China National Knowledge Infrastructure (CNKI), Weipu (VIP), and WanFang databases to identify original studies published from inception to August 20, 2021. Studies reporting the diagnostic performances of CEUS and CECT for differentiating MFP from PDAC were included. The meta-analysis was performed with Stata 15.0 software. The outcomes included the pooled sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves of CEUS and CECT. Meta-regression was conducted to investigate heterogeneity. Bayesian network meta-analysis was conducted to indirectly compare the overall diagnostic performance.

RESULTS

Twenty-six studies with 2115 pancreatic masses were included. The pooled sensitivity and specificity of CEUS for MFP were 82% (95% confidence interval [CI], 73%-88%; I2  = 0.00%) and 95% (95% CI, 90%-97%; I2  = 63.44%), respectively; the overall +LR, -LR, and DOR values were 15.12 (95% CI, 7.61-30.01), 0.19 (95% CI, 0.13-0.29), and 78.91 (95% CI, 30.94-201.27), respectively; and the area under the SROC curve (AUC) was 0.90 (95% CI, 0.87-92). However, the overall sensitivity and specificity of CECT were 81% (95% CI, 75-85%; I2  = 66.37%) and 94% (95% CI, 90-96%; I2  = 74.87%); the overall +LR, -LR, and DOR values were 12.91 (95% CI, 7.86-21.20), 0.21 (95% CI, 0.16-0.27), and 62.53 (95% CI, 34.45-113.51), respectively; and, the SROC AUC was 0.92 (95% CI, 0.90-0.94). The overall diagnostic accuracy of CEUS was comparable to that of CECT for the differential diagnosis of MFP and PDAC (relative DOR 1.26, 95% CI [0.42-3.83], P  > 0.05).

CONCLUSIONS

CEUS and CECT have comparable diagnostic performance for differentiating MFP from PDAC, and should be considered as mutually complementary diagnostic tools for suspected focal pancreatic lesions.

Multiparameter diagnostic model based on 18F-FDG positron emission tomography and serological examination for differentiating focal autoimmune pancreatitis from pancreatic ductal adenocarcinoma

Background

18F-fluorodeoxyglucose positron emission tomography-computerized tomography (18F-FDG PET-CT) has demonstrated high sensitivity in the diagnosis of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC), while also exhibiting the ability to distinguish AIP from PDAC lesions. The objective of this investigation was to assess the efficacy of multiparametric 18F-FDG PET with serological examination for distinguishing focal AIP (f-AIP) from PDAC.

Methods

A total of 127 patients (43 with f-AIP and 84 with PDAC) who received 18F-FDG PET-CT before treatment were retrospectively included in the cohort study conducted at two centers, Beijing Friendship Hospital and Chinese PLA General Hospital, from January 2015 to December 2021. The baseline characteristics and clinical data were collected. The metabolism parameters of 18F-FDG PET, including maximum standardized uptake value (SUVmax), tumor-to-normal liver SUV ratio (SUVR), mean SUV (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were evaluated. The area under the receiver operating characteristic (ROC) curve was used to evaluate the differential diagnostic efficacy. The diagnostic efficacy improvement was assessed through the integrated discriminatory improvement (IDI), net reclassification improvement (NRI), and DeLong test.

Results

Serum immunoglobulin G4 (IgG4) >280 mg/dL, carbohydrate antigen 19-9 (CA19-9) <85 U/mL, and metabolic parameters differed significantly between patients with f-AID and PDAC. The ROC curve analysis of MTV showed the highest differentiating diagnostic value [sensitivity =0.814, 95% confidence interval (CI): 0.661-0.911; specificity =0.893, 95% CI: 0.802-0.947; area under the curve (AUC) =0.890, 95% CI: 0.820-0.957]. The combined diagnostics model of serum IgG4 >280 mg/dL, CA19-9 <85 U/mL, and MTV resulted in the highest AUC of 0.991 (95% CI: 0.978-1.000; sensitivity =0.953, 95% CI: 0.829-0.992; specificity =0.964, 95% CI: 0.892-0.991).

Conclusions

The multiparameter diagnostic model based on 18F-FDG PET and serological examination has excellent clinical value in the differential diagnosis of f-AID and PDAC.

Pancreatic CT perfusion: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters

BACKGROUND

This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results.

METHODS

Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters.

RESULTS

Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05).

CONCLUSIONS

Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols.

CRITICAL RELEVANCE STATEMENT

Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice.

Dynamic contrast enhanced ultrasound in gastrointestinal diseases: A current trend or an indispensable tool?

Contrast enhanced ultrasound (CEUS) has been widely implemented in clinical practice because of the enormous quantity of information it provides, along with its low cost, reproducibility, minimal invasiveness, and safety of the second-generation ultrasound contrast agents. To overcome the limitation of CEUS given by the subjective evaluation of the contrast enhancement behaviour, quantitative analysis of contrast kinetics with generation of time-intensity curves has been introduced in recent years. The quantification of perfusion parameters [named as dynamic-CEUS (D-CEUS)] has several applications in gastrointestinal neoplastic and inflammatory disorders. However, the limited availability of large studies and the heterogeneity of the technologies employed have precluded the standardisation of D-CEUS, which potentially represents a valuable tool for clinical practice in management of gastrointestinal diseases. In this article, we reviewed the evidence exploring the application of D-CEUS in gastrointestinal diseases, with a special focus on liver, pancreas, and inflammatory bowel diseases.

Pancreatic cancer, autoimmune or chronic pancreatitis, beyond tissue diagnosis: Collateral imaging and clinical characteristics may differentiate them

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies and is developing into the 2^nd leading cause of cancer-related death. Often, the clinical and radiological presentation of PDAC may be mirrored by other inflammatory pancreatic masses, such as autoimmune pancreatitis (AIP) and mass-forming chronic pancreatitis (MFCP), making its diagnosis challenging. Differentiating AIP and MFCP from PDAC is vital due to significant therapeutic and prognostic implications. Current diagnostic criteria and tools allow the precise differentiation of benign from malignant masses; however, the diagnostic accuracy is imperfect. Major pancreatic resections have been performed in AIP cases under initial suspicion of PDAC after a diagnostic approach failed to provide an accurate diagnosis. It is not unusual that after a thorough diagnostic evaluation, the clinician is confronted with a pancreatic mass with uncertain diagnosis. In those cases, a re-evaluation must be entertained, preferably by an experienced multispecialty team including radiologists, pathologists, gastroenterologists, and surgeons, looking for disease-specific clinical, imaging, and histological hallmarks or collateral evidence that could favor a specific diagnosis. Our aim is to describe current diagnostic limitations that hinder our ability to reach an accurate diagnosis among AIP, PDAC, and MFCP and to highlight those disease-specific clinical, radiological, serological, and histological characteristics that could support the presence of any of these three disorders when facing a pancreatic mass with uncertain diagnosis after an initial diagnostic approach has been unsuccessful.

CA 19-9 but Not IGF-1/IGFBP-2 Is a Useful Biomarker for Pancreatic Ductal Adenocarcinoma (PDAC) and Chronic Pancreatitis (CP) Differentiation

INTRODUCTION

There are still no effective diagnostic and prognostic biomarkers in pancreatic ductal adenocarcinoma (PDAC). The differentiation between PDAC and chronic pancreatitis (CP) is often challenging. The inflammatory mass in the course of CP causes diagnostic difficulties in differentiating them from neoplastic lesions and, thus, delays the initiation of radical treatment. Insulin-like growth factor 1 (IGF-1) and insulin-like growth factor-binding protein 2 (IGFBP-2) form a network involved in PDAC development. The role of IGFs in promoting pancreatic cancer cell proliferation, survival, and migration is well established, and their ability to stimulate tumor growth and metastasis is well documented. The aim of the study was to evaluate the usability of IGF-1, IGFBP-2, and IGF-1/IGFBP-2 ratio in PDAC and CP differentiation.

MATERIAL AND METHODS

The study included 137 patients: 89 patients with PDAC and 48 patients with CP. All subjects were tested for the levels of IGF-1 and IGFBP-2 using the ELISA method (Corgenix UK Ltd. R&D Systems), along with the level of CA 19-9 in serum. Additionally, the IGF-1/IGFBP-2 ratio was calculated. Further analyses used logit and probit models with varying determinants in order to discern between PDAC and CP patients. The models served as a basis for AUROC calculation.

RESULTS

The mean IGF-1 serum level was equal to 52.12 ± 33.13 ng/mL in PDAC vs. 74.23 ± 48.98 ng/mL in CP (p = 0.0053). The mean level of IGFBP-2 was equal to 305.95 ± 194.58 ng/mL in PDAC vs. 485.43 ± 299 ng/mL in CP (p = 0.0002). The mean CA 19-9 serum concentration was 434.95 ± 419.98 U/mL in PDAC vs. 78.07 ± 182.36 U/mL in CP (p = 0.0000). The mean IGF-1/IGFBP-2 ratio was 0.213 ± 0.14 in PDAC vs. 0.277 ± 0.33 in CP (p = 0.1914). The diagnostic usefulness of indicators for the purpose of PDAC and CP differentiation was assessed by means of AUROC comparison. The AUROCs of IGF-1, IGFBP-2, and IGF-1/IGFBP-2 ratio ranged below 0.7, being lower than the AUROC of CA 19-9 (0.7953; 0.719 within 95% CI). Together, the CA 19-9 and IGFBP-2 AUROCs also ranged below 0.8. When age was included, the AUROC increased to 0.8632, and its 95% confidence interval held above the 0.8 limit. The sensitivity of the used markers was not correlated to the stage of pancreatic PDAC.

CONCLUSIONS

The presented results indicate that CA 19-9 is a marker demonstrating high potential for PDAC and CP differentiation. The inclusion of additional variables into the model, such as the serum level of IGF-1 or IGFBP-2, slightly increased the sensitivity in differentiating CP from PDAC. The IGF-1/IGFBP-2 ratio turned out to be a good marker of pancreatic diseases, but insufficient for the purpose of CP and PDAC differentiation.

Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

PURPOSE

To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed.

METHODS

A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort.

RESULTS

In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUC_LASSO = 0.743, 95% CI: 0.590-0.896; AUC_PCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUC_onlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUC_PCACli = 0.880, 95% CI: 0.776-0.983; AUC_LASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUC_FNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%.

CONCLUSIONS

The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.

Exploring the Potential of Three-Dimensional Imaging, Printing, and Modeling in Pediatric Surgical Oncology: A New Era of Precision Surgery

Pediatric surgical oncology is a technically challenging field that relies on CT and MRI as the primary imaging tools for surgical planning. However, recent advances in 3D reconstructions, including Cinematic Rendering, Volume Rendering, 3D modeling, Virtual Reality, Augmented Reality, and 3D printing, are increasingly being used to plan complex cases bringing new insights into pediatric tumors to guide therapeutic decisions and prognosis in different pediatric surgical oncology areas and locations including thoracic, brain, urology, and abdominal surgery. Despite this, challenges to their adoption remain, especially in soft tissue-based specialties such as pediatric surgical oncology. This work explores the main innovative imaging reconstruction techniques, 3D modeling technologies (CAD, VR, AR), and 3D printing applications through the analysis of three real cases of the most common and surgically challenging pediatric tumors: abdominal neuroblastoma, thoracic inlet neuroblastoma, and a bilateral Wilms tumor candidate for nephron-sparing surgery. The results demonstrate that these new imaging and modeling techniques offer a promising alternative for planning complex pediatric oncological cases. A comprehensive analysis of the advantages and limitations of each technique has been carried out to assist in choosing the optimal approach.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review

BACKGROUND AND AIMS

Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases.

METHODS

Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist.

RESULTS

A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points.

CONCLUSIONS

DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

The Rate of Avoidable Pancreatic Resections at a High-Volume Center: An Internal Quality Control and Critical Review

BACKGROUND

The incidence of benign diseases among pancreatic resections for suspected malignancy still represents a relevant issue in the surgical practice. This study aims to identify the preoperative pitfalls that led to unnecessary surgeries at a single Austrian center over a twenty-year period.

METHODS

Patients undergoing surgery for suspected pancreatic/periampullary malignancy between 2000 and 2019 at the Linz Elisabethinen Hospital were included. The rate of "mismatches" between clinical suspicion and histology was considered as primary outcome. All cases that, despite that, fulfilled the indication criteria for surgery were defined as minor mismatches (MIN-M). Conversely, the true avoidable surgeries were identified as major mismatches (MAJ-M).

RESULTS

Among the 320 included patients, 13 (4%) presented with benign lesions at definitive pathology. The rate of MAJ-M was 2.8% (n = 9), and the most frequent causes of misdiagnoses were autoimmune pancreatitis (n = 4) and intrapancreatic accessory spleen (n = 2). In all MAJ-M cases, various mistakes within the preoperative workup were detected: lack of multidisciplinary discussion (n = 7, 77.8%); inappropriate imaging (n = 4, 44.4%); lack of specific blood markers (n = 7, 77.8%). The morbidity and mortality rates for mismatches were 46.7% and 0.

CONCLUSION

All avoidable surgeries were the result of an incomplete pre-operative workup. The adequate identification of the underlying pitfalls could lead to minimize and, potentially, overcome this phenomenon with a concrete optimization of the surgical-care process.

Pancreatic Cancer in Chronic Pancreatitis: Pathogenesis and Diagnostic Approach

Chronic pancreatitis is one of the main risk factors for pancreatic cancer, but it is a rare event. Inflammation and oncogenes work hand in hand as key promoters of this disease. Tobacco is another co-factor. During alcoholic chronic pancreatitis, the cumulative risk of cancer is estimated at 4% after 15 to 20 years. This cumulative risk is higher in hereditary pancreatitis: 19 and 12% in the case of PRSS1 and SPINK1 mutations, respectively, at an age of 60 years. The diagnosis is difficult due to: (i) clinical symptoms of cancer shared with those of chronic pancreatitis; (ii) the parenchymal and ductal remodeling of chronic pancreatitis rendering imaging analysis difficult; and (iii) differential diagnoses, such as pseudo-tumorous chronic pancreatitis and paraduodenal pancreatitis. Nevertheless, the occurrence of cancer during chronic pancreatitis must be suspected in the case of back pain, weight loss, unbalanced diabetes, and jaundice, despite alcohol withdrawal. Imaging must be systematically reviewed. Endoscopic ultrasound-guided fine-needle biopsy can contribute by targeting suspicious tissue areas with the help of molecular biology (search for KRAS, TP53, CDKN2A, DPC4 mutations). Short-term follow-up of patients is necessary at the clinical and paraclinical levels to try to diagnose cancer at a surgically curable stage. Pancreatic surgery is sometimes necessary if there is any doubt.

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma

Background and aims

Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.

Methods

The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow F_p, plasma volume fraction v_p, transfer constant K^trans, and extravascular extracellular volume fraction v_e. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.

Results

Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of F_p, v_p, K^trans, and v_e can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.

Conclusion

Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

Independent Validation and Assay Standardization of Improved Metabolic Biomarker Signature to Differentiate Pancreatic Ductal Adenocarcinoma From Chronic Pancreatitis

BACKGROUND & AIMS

Pancreatic ductal adenocarcinoma cancer (PDAC) is a highly lethal malignancy requiring efficient detection when the primary tumor is still resectable. We previously developed the MxPancreasScore comprising 9 analytes and serum carbohydrate antigen 19-9 (CA19-9), achieving an accuracy of 90.6%. The necessity for 5 different analytical platforms and multiple analytical runs, however, hindered clinical applicability. We therefore aimed to develop a simpler single-analytical run, single-platform diagnostic signature.

METHODS

We evaluated 941 patients (PDAC, 356; chronic pancreatitis [CP], 304; nonpancreatic disease, 281) in 3 multicenter independent tests, and identification (ID) and validation cohort 1 (VD1) and 2 (VD2) were evaluated. Targeted quantitative plasma metabolite analysis was performed on a liquid chromatography-tandem mass spectrometry platform. A machine learning-aided algorithm identified an improved (i-Metabolic) and minimalistic metabolic (m-Metabolic) signatures, and compared them for performance.

RESULTS

The i-Metabolic Signature, (12 analytes plus CA19-9) distinguished PDAC from CP with area under the curve (95% confidence interval) of 97.2% (97.1%-97.3%), 93.5% (93.4%-93.7%), and 92.2% (92.1%-92.3%) in the ID, VD1, and VD2 cohorts, respectively. In the VD2 cohort, the m-Metabolic signature (4 analytes plus CA19-9) discriminated PDAC from CP with a sensitivity of 77.3% and specificity of 89.6%, with an overall accuracy of 82.4%. For the subset of 45 patients with PDAC with resectable stages IA-IIB tumors, the sensitivity, specificity, and accuracy were 73.2%, 89.6%, and 82.7%, respectively; for those with detectable CA19-9 >2 U/mL, 81.6%, 88.7%, and 84.5%, respectively; and for those with CA19-9 <37 U/mL, 39.7%, 94.1%, and 76.3%, respectively.

CONCLUSIONS

The single-platform, single-run, m-Metabolic signature of just 4 metabolites used in combination with serum CA19-9 levels is an innovative accurate diagnostic tool for PDAC at the time of clinical presentation, warranting further large-scale evaluation.

Mass-Forming Chronic Pancreatitis: Diagnostic Performance of PET/CT

Mass-forming chronic pancreatitis and pancreatic ductal adenocarcinoma are most commonly located in the head of pancreas, and there is a marked overlap in clinical features and imaging findings that makes it diagnostically challenging, although prognosis and management of both these entities differ. Differentiation is made even more difficult when surgical exploratory biopsy is obtained. Radical surgical resection remains the standard of care for pancreatic ductal adenocarcinoma and conservative treatment is effective for mass-forming chronic pancreatitis. Misdiagnosis of mass-forming chronic pancreatitis as pancreatic ductal adenocarcinoma results in unnecessary surgical intervention, and misdiagnosis of pancreatic ductal adenocarcinoma as mass-forming chronic pancreatitis results in delay in surgical intervention when required. Fluorodeoxyglucose-positron emission tomography/computed tomography can reliably be used for tissue characterization of mass-forming chronic pancreatitis and for monitoring disease response following treatment. Although differentiation of mass-like lesions of pancreas is reliably made on histopathology, significant false-negative rate is a major drawback that has a negative effect on diagnosis. This case report describes a rare presentation of mass-forming chronic pancreatitis with florid dystrophic calcifications in a 60-year-old male.

Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis

OBJECTIVES

To develop and validate a CT nomogram and a radiomics nomogram to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) in patients with chronic pancreatitis (CP).

METHODS

In this retrospective study, the data of 138 patients with histopathologically diagnosed MFCP or PDAC treated at our institution were retrospectively analyzed. Two radiologists analyzed the original cross-sectional CT images based on predefined criteria. Image segmentation, feature extraction, and feature reduction and selection were used to create the radiomics model. The CT and radiomics models were developed using data from a training cohort of 103 consecutive patients. The models were validated in 35 consecutive patients. Multivariable logistic regression analysis was conducted to develop a model for the differential diagnosis of MFCP and PDAC and visualized as a nomogram. The nomograms' performances were determined based on their differentiating ability and clinical utility.

RESULTS

The mean age of patients was 53.7 years, 75.4% were male. The CT nomogram showed good differentiation between the two entities in the training (area under the curve [AUC], 0.87) and validation (AUC, 0.94) cohorts. The radiomics nomogram showed good differentiation in the training (AUC, 0.91) and validation (AUC, 0.93) cohorts. Decision curve analysis showed that patients could benefit from the CT and radiomics nomograms, if the threshold probability was 0.05-0.85 and > 0.05, respectively.

CONCLUSIONS

The two nomograms reasonably accurately differentiated MFCP from PDAC in patients with CP and hold potential for refining the management of pancreatic masses in CP patients.

KEY POINTS

• A CT nomogram and a computed tomography-based radiomics nomogram reasonably accurately differentiated mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis (CP). • The two nomograms can monitor the cancer risk in patients with CP and hold promise to optimize the management of pancreatic masses in patients with CP.

Stocky/Packed Pancreas: A Case of Focal Drug-Induced Acute Pancreatitis Mimicking Cancer

Drug-induced acute pancreatitis (DIP) is a recognised but underreported entity in the literature. Immunotherapy drugs have been described as one possible emerging cause, although the pathogenic mechanism is still largely unclear. To date, only a few cases have been reported, even if in recent times there is an over-increasing awareness of this pathologic entity. The imaging-based diagnosis of DIP can be difficult to establish, representing a real challenge for a radiologist, especially when the inflammatory disease appears as a focal mass suspicious for a malignancy. Case report: We herein report the case of a 71-year-old man with a known history of partially responsive lung adenocarcinoma subtype with high programmed cell death ligand 1 (PD-L1) expression, who underwent positron emission tomography (PET)/computed tomography (CT) imaging follow-up after one year of immunotherapy. The exam revealed a stocky/packed lesion in the pancreatic body, with increased 18F-fluorodeoxyglucose (FDG) accumulation highly suggestive of pancreatic cancer, which finally was proven to be a DIP induced by immunotherapy. Conclusion: Distinguishing between focal DIP and pancreatic neoplasm is, therefore, crucial for timely therapeutic management and prognostic stratification. A deep knowledge of possible imaging pitfalls coupled with a comprehensive clinical and laboratory assessment is pivotal to avoid any delays in diagnosis.

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.

Predicting Factors for Pancreatic Malignancy with Computed Tomography and Endoscopic Ultrasonography in Chronic Pancreatitis

Diagnosing pancreatic malignancy is challenging, especially in patients with chronic pancreatitis (CP). Endoscopic ultrasonography (EUS) is a promising diagnostic procedure for discriminating between malignancy and CP. We aimed to investigate the predictive factors and reliability of computed tomography (CT) and EUS for differentiating pancreatic mass lesions and the diagnostic accuracy of EUS-FNA or FNB in patients with CP. Forty patients with CP, receiving CT and EUS-FNA or FNB for pancreatic mass lesion evaluation, were enrolled in the study. Patients’ data, CT and EUS characteristics, image-based diagnosis, cytopathology, and final diagnosis were recorded. EUS was superior to CT in terms of diagnostic accuracy (92.5% vs. 82.5%, p = 0.02). Both CT and EUS showed significant predictive factors (all p < 0.05) with the tumor image hypoattenuation pattern or vessel invasion on CT and pancreatic duct dilatation, or distal pancreatic atrophy on EUS. EUS imaging is a reliable modality for evaluating pancreatic lesions, even with a CP background. The EUS image has a higher diagnostic accuracy than CT. Predicting factors, including hypoechoic pattern, pancreatic duct dilatation, and distal pancreas atrophy, may help to differentiate benign or malignant in patients with CP.

Gender-Specific Coagulation Profiles of Peripheral and Portal Blood May Help to Differentiate Malignant from Benign Pancreatic Tumour-Pilot Study

Objective: Pancreatic adenocarcinoma (PDAC) and mass forming chronic pancreatitis (CP) can be easily misdiagnosed due to their resemblances in clinical, radiological, and biochemical criteria. In our previous study, we reported a very high concentration of D-Dimers in portal blood in patients with pancreatic cancer which may help to differentiate malignant from benign pancreatic tumours. In this study, we aim to describe other portal and peripheral coagulation profiles of PDAC in comparison to CP patients, as well to test the hypothesis; thus, it is possible to distinguish pancreatic malignancy and benign tumour based on these parameters. Methods: We included retrospectively 115 patients with the absence of venous thromboembolism (VTE), qualified to surgical treatment due to pancreatic tumours, both PDAC and CP. Patients underwent surgery in General and Transplant Surgery Unit of Medical University of Lodz between December 2011 and February 2014. Patients with distant metastases diagnosed before or during the surgery were excluded. The coagulation profile, which includes fibrinogen, activated partial thromboplastin time (aPTT), prothrombin time (PT), and thrombin time (TT), was determined in blood samples from the portal and peripheral vein taken intraoperatively. Results: The fibrinogen level was higher and the aPTT index shortened in the peripheral and portal blood of the PDAC group, which reflects the well-known link between PDAC and general hypercoagulability. Furthermore, these effects are sex-specific. The mean age in the CP group was lower than in the PDAC group (54.63 ± 12.37 vs. 63.77 ± 3.23, p < 0.001) and correlated with the fibrinogen distribution in male patients with CP (portal r = 0.34; p = 0.07; peripheral r = 0.39; p = 0.04). We calculated sex-specific logistic regression models (male: peripheral aPTT and age, AUC: 0.795, female: portal fibrinogen and age, AUC: 0.805), both maintaining the good discrimination properties after V-fold cross validation (0.759, 0.742). Conclusions: Our study shows that the differences between coagulation profiles in PDAC and CP patients not only seems to be a reflection of gender-specific biological features, but also helps to discriminate between them. The main goal of the study was to explore the biology of pancreatic cancer and lay a solid base for further investigations of PDAC biomarkers. This paper is the first to describe the detailed coagulation profile in portal blood in patients with pancreatic solid tumors. At present, the clinical application of our results is not clear; however, we hope that it may improve our understanding of this complex disease.

GaWRDenMap: a quantitative framework to study the local variation in cell-cell interactions in pancreatic disease subtypes

Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. Quantification of heterogeneity in the tumor microenvironment is extremely important in pancreatic ductal adenocarcinoma (PDAC), which has been shown to co-occur with other pancreatic diseases and neoplasms with certain attributes that make visual discrimination difficult. In this paper, we propose the GaWRDenMap framework, that utilizes the concepts of geographically weighted regression (GWR) and a density function-based classification model, and apply it to a cohort of multiplex immunofluorescence images from patients belonging to six different pancreatic diseases. We used an internal cohort of 228 patients comprised of 34 Chronic Pancreatitis (CP), 71 PDAC, 70 intraductal papillary mucinous neoplasm (IPMN), 16 mucinous cystic neoplasm (MCN), 29 pancreatic intraductal neoplasia (PanIN) and 8 IPMN-associated PDAC patients. We utilized GWR to model the relationship between epithelial cells and immune cells on a spatial grid. The GWR model estimates were used to generate density signatures which were used in subsequent pairwise classification models to distinguish between any two pairs of disease groups. Image-level, as well as subject-level analysis, were performed. When applied to this dataset, our classification model showed significant discrimination ability in multiple pairwise comparisons, in comparison to commonly used abundance-based metrics, like the Morisita-Horn index. The model was able to best discriminate between CP and PDAC at both the subject- and image-levels. It was also able to reasonably discriminate between PDAC and IPMN. These results point to a potential difference in the spatial arrangement of epithelial and immune cells between CP, PDAC and IPMN, that could be of high diagnostic significance. Further validation on a more comprehensive dataset would be warranted.

Comparative analysis of two mathematical algorithms for the calculation of computed tomography perfusion parameters in the healthy and diseased pancreas

BACKGROUND

The maximum slope (MS) and deconvolution (DC) algorithms are commonly used to post-process computed tomography perfusion (CTP) data. This study aims to analyze the differences between MS and DC algorithms for the calculation of pancreatic CTP parameters.

METHODS

The pancreatic CTP data of 57 patients were analyzed using MS and DC algorithms. Two blinded radiologists calculated pancreatic blood volume (BV) and blood flow (BF). Interobserver correlation coefficients were used to evaluate the consistency between two radiologists. Paired t-tests, Pearson linear correlation analysis, and Bland-Altman analysis were performed to evaluate the correlation and consistency of the CTP parameters between the two algorithms.

RESULTS

Among the 30 subjects with normal pancreas, the BV values in the three pancreatic regions were higher in the case of the MS algorithm than in the case of the DC algorithm (t = 39.35, p < 0.001), and the BF values in the three pancreatic regions were slightly higher for the MS algorithm than for the DC algorithm (t = 2.19, p = 0.031). Similarly, among the 27 patients with acute pancreatitis, the BV values obtained using the MS methods were higher than those obtained using the DC methods (t = 54.14, p < 0.001). Furthermore, the BF values were higher with the MS methods than the DC methods (t = 8.45, p < 0.001). Besides, Pearson linear correlation and Bland-Altman analysis showed that the BF and BV values showed a good correlation and a bad consistency between the two algorithms.

CONCLUSIONS

The BF and BV values measured using MS and DC algorithms had a good correlation but were not consistent.

Pancreatitis and pancreatic cancer: A case of the chicken or the egg

Acute pancreatitis (AP), chronic pancreatitis (CP) and pancreatic cancer are three distinct pancreatic diseases with different prognoses and treatment options. However, it may be difficult to differentiate between benign and malignant disease. AP may be a first symptom of pancreatic cancer, particularly in patients between the ages of 56 and 75 with presumed idiopathic AP who had a concomitant diagnosis of new-onset diabetes mellitus or patients who present with CP at diagnosis of AP. In these patients, additional imaging is warranted, preferably by endoscopic ultrasonography. CP may lead to pancreatic cancer through oncogenic mutations, mostly in patients with hereditary CP, and in patients in whom risk factors for pancreatic cancer (e.g., nicotine and alcohol abuse) are also present. Patients with PRSS1-mediated CP and patients with a history of autosomal dominant hereditary CP without known genetic mutations may be considered for surveillance for pancreatic cancer. Pancreatic inflammation may mimic pancreatic cancer by appearing as a focal mass-forming lesion on imaging. Differentiation between the above mentioned benign and malignant disease may be facilitated by specific features like the duct-penetrating sign and the duct-to-parenchyma ratio. Research efforts are aimed towards developing a superior discriminant between pancreatitis and pancreatic cancer in the form of imaging modalities or biomarkers. This may aid clinicians in timely diagnosing pancreatic cancer in a potentially curable stage.

Imaging of inflammatory disease of the pancreas

Increasingly acute and chronic pancreatitis (AP and CP) are considered a continuum of a single entity. Nonetheless, if, after flare-up, the pancreas shows no residual inflammation, it is classified as AP. CP is characterised by a long cycle of worsening and waning glandular inflammation without the pancreas ever returning to its baseline structure or function. According to the International Consensus Guidelines on Early Chronic Pancreatitis, pancreatic inflammation must last at least 6 months before it can be labelled CP. The distinction is important because, unlike AP, CP can destroy endocrine and exocrine pancreatic function, emphasising the importance of early diagnosis. As typical AP can be diagnosed by clinical symptoms plus laboratory tests, imaging is usually reserved for those with recurrent, complicated or CP. Imaging typically starts with ultrasound and more frequently with contrast-enhanced computed tomography (CECT). MRI and/or MR cholangiopancreatography can be used as a problem-solving tool to confirm indirect signs of pancreatic mass, differentiate between solid and cystic lesions, and to exclude pancreatic duct anomalies, as may occur with recurrent AP, or to visualise early signs of CP. MR cholangiopancreatography has replaced diagnostic endoscopic retrograde cholangiopancreatography (ERCP). However, ERCP, and/or endoscopic ultrasound (EUS) remain necessary for transpapillary biliary or pancreatic duct stenting and transgastric cystic fluid drainage or pancreatic tissue sampling, respectively. Finally, positron emission tomography-MRI or positron emission tomography-CT are usually reserved for complicated cases and/or to search for extra pancreatic systemic manifestations. In this article, we discuss a broad spectrum of inflammatory pancreatic disorders and the utility of various modalities in diagnosing acute and chronic pancreatitis.

Benign vs malignant pancreatic lesions: Molecular insights to an ongoing debate

Several benign conditions such as chronic pancreatitis, autoimmune pancreatitis, and paraduodenal pancreatitis can present as mass lesions and may mimic pancreatic ductal adenocarcinoma (PDAC) clinically and radiologically. Thorough histologic examination with attention to certain morphologic features can assist in deciphering neoplastic from reactive, however small biopsies often remain a challenge. Variable histologic patterns in conventional PDAC may also confound the diagnosis of PDAC. Uncommon subtypes of pancreatic carcinoma such as adenosquamous and squamous cell carcinoma, colloid carcinoma, medullary carcinoma, hepatoid carcinoma and signet ring cell carcinoma necessitate excluding metastasis from other sites prior to rendering the diagnosis of pancreatic carcinoma. The use of immunohistochemical staining and molecular markers can aid in separating benign from malignant and PDAC from metastasis. PDAC expresses a few non-specific epithelial and mucin immunomarkers such as CK7, CK19, MUC1, MUC4 and MUC5AC. However, the only immunohistochemical marker that is specific for PDAC in the right clinical context is SMAD4. Loss of SMAD4 within atypical glands and ducts supports the diagnosis of PDAC in a limited sample. Unfortunately, this finding is seen only in 50% of PDAC cases. The identification of certain mutations can help support a diagnosis of PDAC when benign conditions are in the differential. At the molecular level, KRAS oncogene mutations are seen in approximately 93% of PDACs. Subsequent neoplastic progression is driven by additional mutations of tumor suppressor genes, such as CDKN2A, TP53, and SMAD4. Molecular markers can also provide an insight to the prognosis. For instance, the loss of SMAD4 is associated with a poor outcome whereas mutations in MLL, MLL2, MLL3, and ARID1A are associated with improved survival.

[Chronic pancreatitis : Characterization and differentiation from pancreatic cancer]

Klinisches/methodisches Problem

Bei der chronischen Pankreatitis (CP) handelt es sich um eine langanhaltende Entzündung der Bauchspeicheldrüse, welche die normale Struktur und Funktion des Organs schädigt. Das breite Spektrum an entzündlichen Pankreaserkrankungen umfasst einzelne Entitäten, wie die fokale Pankreatitis (FP) oder den Pseudotumor („mass-forming pancreatitis“), welche radiomorphologisch ein Adenokarzinom der Bauchspeicheldrüse (PDAC) nachahmen können. In weiterer Folge kann eine Fehldiagnose zu einem vermeidbaren und unnötigen operativen Eingriff oder zu einer Therapieverzögerung führen.

Radiologische Standardverfahren

Der Ultraschall (US) ist das primäre bildgebende Verfahren zur Abklärung von Pankreaserkrankungen, gefolgt von kontrastmittelverstärkter Computertomographie (KM-CT), die als meistverwendete Methode bei der diagnostischen Abklärung von Bauchspeicheldrüsenerkrankungen gilt. Die Magnetresonanztomographie (MRT) und/oder die MR-Cholangiopankreatographie (MRCP) können als Problemlöser eingesetzt werden, um zwischen soliden und zystischen Läsionen zu unterscheiden sowie auch Anomalien der Pankreasgänge auszuschließen, welche bei rezidivierender akuter Pankreatitis (AP) vorhanden sein können, oder um frühe Anzeichen einer CP zu visualisieren. Die MRCP hat dabei die diagnostische endoskopische retrograde Cholangiopankreatographie (ERCP) in der Abklärung von therapeutischen Interventionen im Wesentlichen ersetzt.

Empfehlung für die Praxis

Folgender Übersichtsartikel fasst die relevanten Merkmale in der Computertomographie (CT) und MRT zusammen, um eine akkurate, frühzeitige Diagnose einer CP zu stellen und eine Differenzierung zwischen FP und Pankreaskarzinom zu ermöglichen, um somit – auch in schwierigen Fällen – ein adäquates Therapiemanagement zu gewährleisten.