Mass-forming Chronic Pancreatitis Research Articles

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

A Deep Learning Framework for Mass-Forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma Classification Based on Magnetic Resonance Imaging

Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application.

Radiomics allows the extraction of quantitative imaging features from clinical magnetic resonance imaging (MRI) and computerized tomography (CT) studies. The advantages of radiomics have primarily been exploited in oncological applications, including better characterization and staging of oncological lesions and prediction of patient outcomes and treatment response. The potential introduction of radiomics in the clinical setting requires the establishment of a standardized radiomics pipeline and a quality assurance program. Radiomics and texture analysis of the liver have improved the differentiation of hypervascular lesions such as adenomas, focal nodular hyperplasia, and hepatocellular carcinoma (HCC) during the arterial phase, and in the pretreatment determination of HCC prognostic factors (e.g., tumor grade, microvascular invasion, Ki-67 proliferation index). Radiomics of pancreatic CT and MR images has enhanced pancreatic ductal adenocarcinoma detection and its differentiation from pancreatic neuroendocrine tumors, mass-forming chronic pancreatitis, or autoimmune pancreatitis. Radiomics can further help to better characterize incidental pancreatic cystic lesions, accurately discriminating benign from malignant intrapancreatic mucinous neoplasms. Nonetheless, despite their encouraging results and exciting potential, these tools have yet to be implemented in the clinical setting. This non-systematic review will describe the essential steps in the implementation of the radiomics and feature extraction workflow from liver and pancreas CT and MRI studies for their potential clinical application. A succinct overview of reported radiomics applications in the liver and pancreas and the challenges and limitations of their implementation in the clinical setting is also discussed, concluding with a brief exploration of the future perspectives of radiomics in the gastroenterology field.

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

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. 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. 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. 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.

Utility of Contrast-Enhanced Computed Tomography for Differentiating Pancreatic Ductal Adenocarcinoma from Inflammatory Mass in Chronic Calcific Pancreatitis

Abstract Objective The aim of this study was to identify the most useful contrast-enhanced computed tomography (CECT) features for differentiating pancreatic ductal adenocarcinoma (PDAC) from mass-forming chronic pancreatitis (MFCP) in chronic calcific pancreatitis (CCP). Methods In total, 101 patients with CCP and focal pancreatic mass formed the study group. Sixteen qualitative and four quantitative parameters were analyzed. Qualitative parameters included size, site, margin, intralesional hypodensity, collateral duct sign, abrupt pancreatic duct (PD) cutoff, upstream PD dilatation, distal pancreatic atrophy, double duct sign, enhancement pattern, contrast attenuation, peripancreatic inflammation, vascular involvement, regional nodes, and metastasis. Quantitative parameters included duct-to-body ratio, common bile duct (CBD) diameter, main pancreatic duct (MPD) diameter, and carcinoembryonic antigen 19-9 (CA19-9). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for prediction of PDAC. A receiver operating characteristic (ROC) curve analysis was performed and the area under ROC curve (AUROC) was calculated to determine diagnostic accuracy to assess the optimal cutoff. Results PDAC was confirmed in 48 patients and MFCP in 53 patients. A duct-to-body ratio greater than 0.48 had 95.5% sensitivity, 83.3% specificity, 80.8% PPV, 96.2% NPV, and 88.5% accuracy for predicting PDAC. A CBD diameter cutoff ≥9.5 mm had an accuracy of 75% (p < 0.019) and an MPD cutoff ≥6.25 mm had an accuracy of 67.8% (p = 0.008) for predicting PDAC. On binary logistic regression, the duct-to-body ratio was found to be the significant independent factor associated with malignancy. Conclusion A duct-to-body ratio greater than 0.48, intralesional hypodensity, and abrupt duct cutoff are the most helpful computed tomography (CT) features for distinguishing PDAC from MFCP in CCP. On binary logistic regression, the duct-to-body ratio was found to be a significant independent factor. Interspersed normal parenchyma was observed as a very specific sign of MFCP. Intraparenchymal hypodensity has high specificity, but further validation is needed.

Differentiating Between Mass-forming Chronic Pancreatitis and Pancreatic Ductal Adenocarcinoma: A Challenging Clinical Approach

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a tumor of the pancreas that has a 5-year survival rate as low as 7.8%. In cases of chronic pancreatitis, it is sometimes challenging to rule out neoplastic changes, as mass-forming pancreatitis (MFCP) that can occur secondary to long-lasting inflammation can commonly mimic the presentation of pancreatic ductal adenocarcinoma. The clinical picture, laboratory, and radiological imaging of PDAC and MFCP may sometimes overlap, resulting in a higher incidence of misdiagnosis and unnecessary surgery. Aim: We aim to describe the various tools available to help physicians distinguish between mass-forming chronic pancreatitis and pancreatic ductal adenocarcinoma. Methods: A literature search was conducted on “PubMed” using the following terms: pancreatic carcinoma, mass-forming chronic pancreatitis, and pancreatic mass. Several articles discussing imaging modalities including ultrasound, CT-scan, and MRI; and laboratory markers including cancer antigen 19–9 (CA 19-9), carcinoembryonic antigen (CEA), glypican-1 (GP-1), low-density lipoprotein receptor (LDLR), and K-RAS, were reviewed. Discussion: Despite their similar presentations, the management of MFCP and PDAC is very different. The similarity in history, clinical symptoms, and imaging findings can lead to unnecessary procedures. In this review, we examined several modalities that physicians might use to avoid any misdiagnosis. Conclusion: Although none of these tests alone has been shown to be superior to the others, a potential suggestion might be to use a combination of these tests to allow a reliable diagnosis.

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis.

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.

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.

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

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). 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. 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. 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. • 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.

Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis

PurposeIt is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions. MethodsA total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts. ResultsADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group. ConclusionThe mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.

Diagnosis of atypical mass-forming chronic pancreatitis mimicking pancreatic carcinoma: A case report

Background: Mass-Forming Chronic Pancreatitis (MFCP) is rare. Moreover, atypical MFCP is difficult to differentiate from Pancreatic Carcinoma (PC) in clinical manifestations, laboratory, and imaging examinations. Diagnosis could be supported by the pathological findings of focal inflammatory fibrosis without evidence of tumor in the pancreas. Case summary: A 52-year-old man had acute pancreatitis twice over 7 months. Amylase and lipase levels were three times higher than the normal range without any clinical symptoms. At the 6th month, the patient lost 15 kg of weight, and abdominal ultrasonography revealed pancreatic head space occupied. All the findings in multimodal imaging including computed tomography image, Magnetic Resonance (MR) imaging with MR cholangiopancreatography, and 18F-FDG positron emission tomography/computed tomography showed an irregular nodule with low density, low signal, and low echo in the head of the pancreas, which were lower than those in the normal pancreatic tissue. The proximal main pancreatic duct was truncated and stenosed, and the distal duct was dilated. Subsequently, he developed progressive painless jaundice, and the specific tumor marker levels were increased. Most of these manifestations were suggestive of the pancreatic malignant tumor; however, multiple specimen pathological findings obtained from laparotomy and endoscopic ultrasonography-guided fine-needle aspiration revealed focal chronic inflammation, fibrosis, and necrosis. Conclusion: This report describes a case of atypical MFCP mimicking PC at clinical presentation and laboratory findings, especially in multimodal imaging. However, the combination of atypical multimodal imaging features, which support MFCP rather than PC, and endoscopic ultrasonography-guided fine-needle aspiration are useful for improving the diagnostic rate of atypical MFCP and avoiding unnecessary surgery.

LDL receptor-peptide conjugate as in vivo tool for specific targeting of pancreatic ductal adenocarcinoma

Despite clinical advances in diagnosis and treatment, pancreatic ductal adenocarcinoma (PDAC) remains the third leading cause of cancer death, and is still associated with poor prognosis and dismal survival rates. Identifying novel PDAC-targeted tools to tackle these unmet clinical needs is thus an urgent requirement. Here we use a peptide conjugate that specifically targets PDAC through low-density lipoprotein receptor (LDLR). We demonstrate by using near-infrared fluorescence imaging the potential of this conjugate to specifically detect and discriminate primary PDAC from healthy organs including pancreas and from benign mass-forming chronic pancreatitis, as well as detect metastatic pancreatic cancer cells in healthy liver. This work paves the way towards clinical applications in which safe LDLR-targeting peptide conjugate promotes tumor-specific delivery of imaging and/or therapeutic agents, thereby leading to substantial improvements of the PDAC patient’s outcome.

Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions.

BackgroundIt is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.MethodsThis retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists’ evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.ResultsThe areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists’ evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.ConclusionsThe radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.

Pancreatitis and PDAC: association and differentiation.

The discrimination of mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) is a central diagnostic dilemma. It is important to differentiate these entities since they have markedly different prognoses and management. Importantly, the appearance of these two entities significantly overlaps on a variety of imaging modalities. However, there are imaging features that may be suggestive of one entity more than the other. MFCP and PDAC may show different enhancement patterns on perfusion computed tomography (CT) and/or dynamic contrast-enhanced MRI (DCE-MRI). The duct-penetrating sign on magnetic resonance cholangiopancreatography (MRCP) is more often associated with MFCP, whereas abrupt cutoff with upstream dilatation of the main pancreatic duct and the double-duct sign (obstruction/cutoff of both the common bile duct and pancreatic duct) are more often associated with PDAC. Nevertheless, tissue sampling is the most reliable method to differentiate between these entities and is currently generally necessary for management.

Chronic Pancreatitis or Pancreatic Tumor? A Problem-solving Approach

Certain inflammatory pancreatic abnormalities may mimic pancreatic ductal adenocarcinoma at imaging, which precludes accurate preoperative diagnosis and may lead to unnecessary surgery. Inflammatory conditions that may appear masslike include mass-forming chronic pancreatitis, focal autoimmune pancreatitis, and paraduodenal pancreatitis or "groove pancreatitis." In addition, obstructive chronic pancreatitis can mimic an obstructing ampullary mass or main duct intraductal papillary mucinous neoplasm. Secondary imaging features such as the duct-penetrating sign, biliary or main pancreatic duct skip strictures, a capsulelike rim, the pancreatic duct-to-parenchyma ratio, displaced calcifications in patients with chronic calcific pancreatitis, the "double duct" sign, and vessel encasement or displacement can help to suggest the possibility of an inflammatory mass or a neoplastic process. An awareness of the secondary signs that favor a diagnosis of malignant or inflammatory lesions in the pancreas can help the radiologist to perform the differential diagnosis and determine the degree of suspicion for malignancy. Repeat biopsy or surgical resection may be necessary to achieve an accurate diagnosis and prevent unnecessary surgery for inflammatory conditions. Online supplemental material and DICOM image stacksare available for this article. ©RSNA, 2019.

An end-to-end pancreatic anastomosis in robotic central pancreatectomy

BackgroundSuturing the proximal pancreatic stump and performing pancreaticoenterostomy for the distal pancreatic stump following central pancreatectomy is a conventional procedure. This reconstruction after resection of the pathological pancreatic lesion brings changes in anatomy and physiology. In this study, an innovative one-stage robotic end-to-end pancreatic anastomosis was reported to replace the conventional pancreaticoenterostomy following central pancreatectomy.Materials and methodsThe clinical data of 11 consecutive patients who underwent robotic central pancreatectomy with end-to-end pancreatic anastomosis between August 2017 and December 2017 were analyzed retrospectively.ResultsAll operations were completed successfully without any conversion to open surgery. Nine patients had benign tumors, one had a mass-forming chronic pancreatitis, and one had an isolated pancreatic metastasis from a renal cancer. The mean gap left after central pancreatectomy was 4.3 ± 1.0 cm. The median operative time was 121 (range, 105 to 199) min. The median blood loss was 50 (range, 20 to 100) ml. Seven (63.6%) patients developed complications which included Clavien–Dindo Grade I complications in five patients, a Grade II complication in one patient, and a Grade IIIa complication in one patient. Seven patients developed a Grade B postoperative pancreatic fistula, and two patients a biochemical leak. There was no Grade C or worse pancreatic fistula. Magnetic resonance cholangiopancreatography at postoperative 6 months showed no stricture in any of the main pancreatic ducts. Three patients had an asymptomatic and small pancreatic pseudocyst.ConclusionRobotic central pancreatectomy with end-to-end pancreatic anastomosis was safe and feasible. It restores the normal anatomy of the pancreas. With its good short-and long-term outcomes, it could be an alternative reconstructive method to pancreaticoenterostomy following central pancreatectomy.

Efficacy of perfusion CT in differentiating of pancreatic ductal adenocarcinoma from mass-forming chronic pancreatitis and characterization of isoattenuating pancreatic lesions.

Multidetector computed tomography (MDCT) is routinely used in the diagnosis of pancreatic ductal adenocarcinoma (PDAC), but it may be inadequate in some cases, especially mass-forming chronic pancreatitis (MFCP) and isoattenuating pancreatic lesions. Perfusion CT (pCT) may help resolve this problem. The aim of this study was to evaluate whether pCT could help differentiating PDAC from MFCP and in characterization of isoattenuating pancreatic lesions. This prospective study included 89 cases of pancreatic lesions detected by MDCT and further analyzed with pCT. Sixty-one cases with final pathological diagnosis PDAC and 12 cases with MFCP were included from the study. Blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface area product (PS) maps were obtained. Perfusion values obtained from the lesions and normal parenchyma were compared. Compared with normal parenchyma, BV, BF, PS were lower and MTT was longer in PDAC and MFCP (p < 0.05). Compared with MFCP, BV, BF, PS were lower and MTT was longer in PDAC (p < 0.001). Compared with normal parenchyma, BV, BF, PS were lower and MTT was longer in isoattenuating lesions, (p < 0.001). Cutoff values of 7.60mL/100mL, 64.43mL/100mL/min, 28.08mL/100mL/min for BV, BF, PS, respectively, provided 100% sensitivity and specificity and 7.47s for MTT provided 98.3% sensitivity, 80% specificity for distinguishing PDAC from MFCP. pCT is a useful technology that can be helpful in overcoming the limitations of routine MDCT in diagnosing PDAC and characterization of isoattenuating lesions.

Multi-modality imaging features distinguish pancreatic carcinoma from mass-forming chronic pancreatitis of the pancreatic head.

The present study retrospectively analyzed computerized tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computerized tomography (PET/CT) data to identify features that may distinguish pancreatic carcinoma (PC) from mass-forming chronic pancreatitis (MFCP) of the pancreatic head. The mean diameter of the lesions was larger in the MFCP patients (n=24) than in the PC patients (n=30; 5.44±27 vs. 3.34±1.23 cm; P<0.001). PC lesions showed increased lobulation when compared with the MFCP cases (83.33 vs. 12.5%; P<0.001). Lesions in the MFCP patients exhibited diffuse and marginally distributed calcification. MFCP patients showed increased exudation around the lesion (83.33 vs. 13.33%), pseudocyst formation (58.33 vs. 10%) and thickening of the right renal fascia (83.33 vs. 13.33%) than in the PC patients. MFCP patients also exhibited visible remnants of normal pancreatic tissue within the lesions. MFCP and PC patients could be distinguished by a cutoff value of 4.40 cm for lesion size [area under the curve (AUC): 0.894; 95% confidence interval (CI): 0.810–0.978)], 21.85 Hu for net-increased value in the arterial phase (AUC, 0.799; 95% CI, 0.670–0.928), 37.70 Hu for net-increased value in the portal phase (AUC, 0.798; 95% CI, 0.919–0.677), 4.85 for early standardized uptake value (SUV) of 18F-deoxyglucose (18F-FDG; AUC, 0.934; 95% CI, 0.850–1.018) and 4.90 for delayed SUV of 18F-FDG (AUC, 0.958; 95% CI, 0.878–1.038). These findings demonstrated that the integration of data from dynamic contrast-enhanced CT, MRI and PET/CT imaging may distinguish MFCP from PC.

Optimized ROI size on ADC measurements of normal pancreas, pancreatic cancer and mass-forming chronic pancreatitis.

ObjectivesTo investigate the effects of region of interest (ROI) sizes on apparent diffusion coefficient (ADC) measurements for the differentiation of normal pancreas (NP), pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP).ResultsThere were no significant differences for the mean ADCs measured by 12 different-size ROIs for MFCP, or PDAC and NP (P = 0.858–1.0). With the increase of ROI size (≥ 55 mm2), ADCs of PDAC were significantly lower than those of NP (all P < 0.05), but there was no difference of the accuracy in ADC for differentiating the two groups only at a ROI size of 214 mm2. When ROI size was above 99 mm2, ADCs of MFCP were significantly lower than those of NP (all P < 0.05). There were no significant differences for any of the mean ADCs measured by 12 different-size ROIs between PDAC and MFCP (P > 0.05).Materials and MethodsDiffusion-weighted imaging (DWI) was performed on 89 participants: 64 with PDAC, 7 with MFCP, as well as 18 healthy volunteers. ADC maps were created using mono-exponential model. A homemade software was used to measure the mean ADC values of 12 concentric round ROIs (areas: 15, 46, 55, 82, 99, 121, 134, 152, 161, 189, 214, 223, and 245 mm2) for the mass of lesions and the NP tissue.ConclusionsIn ADC measurements, the optimized ROI size is 214 mm2 for the differentiation of PDAC and NP; ROI size of ≥ 99 mm2 is recommended to differentiate between MFCP and NP. ADC was not useful for the differentiation of PDAC and MFCP.

Perfusion CT – Can it resolve the pancreatic carcinoma versus mass forming chronic pancreatitis conundrum?

ObjectivesTo evaluate the utility of perfusion CT (PCT) in differentiating pancreatic adenocarcinoma from mass forming chronic pancreatitis (MFCP). MethodsIn this ethically approved study, PCT was performed in 122 patients with pancreatic masses of which 42 patients had pancreatic adenocarcinoma and 13 had MFCP on histopathology. Perfusion parameters studied included blood flow (BF), blood volume (BV), permeability surface area product (PS), time to peak (TTP), peak enhancement intensity (PEI) and mean transit time (MTT). Twenty five controls with no pancreatic pathology were also studied. ResultsAmongst the perfusion parameters BF and BV were found to be the most reliable for differentiating between adenocarcinoma and mass forming pancreatitis. Although they were reduced in both pancreatic adenocarcinoma (BF- 16.6 ± 13.1 ml/100 ml/min and BV- 5 ± 3.5 ml/100 ml) and MFCP (BF- 30.4 ± 8.7 ml/100 ml/min and BV- 8.9 ± 3.1 ml/100 ml) as compared to normal controls (BF- 94.1 ± 24 ml/100 ml/min and BV- 36 ± 10.7 ml/100 ml) but the extent of reduction was greater in pancreatic adenocarcinoma than in MFCP. Based on ROC analysis cut off values of 19.1 ml/100 ml/min for BF and 5 ml/100 ml for BV yielded optimal sensitivity and specificity for differentiating pancreatic adenocarcinoma from MFCP. ConclusionsPCT may serve as an additional paradigm for differentiating pancreatic adenocarcinoma from mass forming chronic pancreatitis and a useful tool for detecting masses which are isodense on conventional CT.

Pancreatic ductal adenocarcinoma and chronic mass-forming pancreatitis: Differentiation with dual-energy MDCT in spectral imaging mode

ObjectiveTo investigate the value of dual-energy MDCT in spectral imaging in the differential diagnosis of chronic mass-forming chronic pancreatitis (CMFP) and pancreatic ductal adenocarcinoma (PDAC) during the arterial phase (AP) and the pancreatic parenchymal phase (PP). Materials and methodsThirty five consecutive patients with CMFP (n=15) or PDAC (n=20) underwent dual-energy MDCT in spectral imaging during AP and PP. Iodine concentrations were derived from iodine-based material-decomposition CT images and normalized to the iodine concentration in the aorta. The difference in iodine concentration between the AP and PP, contrast-to-noise ratio (CNR) and the slope K of the spectrum curve were calculated. ResultsNormalized iodine concentrations (NICs) in patients with CMFP differed significantly from those in patients with PDAC during two double phases (mean NIC, 0.26±0.04mg/mL vs. 0.53±0.02mg/mL, p=0.0001; 0.07±0.02mg/mL vs. 0.28±0.04mg/mL, p=0.0002, respectively). There were significant differences in the value of the slope K of the spectrum curve in two groups during AP and PP (KCMFP=3.27±0.70 vs. KPDAC=1.35±0.41, P=0.001, and KCMFP=3.70±0.17 vs. KPDAC=2.16±0.70, p=0.003, respectively). CNRs at low energy levels (40–70keV) were higher than those at high energy levels (80–40keV). ConclusionIndividual patient CNR-optimized energy level images and the NIC can be used to improve the sensitivity and the specificity for differentiating CMFP from PDAC by use of dual-energy MDCT in spectral imaging with fast tube voltage switching.