Mass-forming autoimmune pancreatitis and pancreatic carcinoma: differential diagnosis on the basis of computed tomography and magnetic resonance cholangiopancreatography, and diffusion-weighted imaging findings

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.

The Role of Endoscopic Ultrasound and Ancillary Techniques in the Diagnosis of Autoimmune Pancreatitis: A Comprehensive Review

Autoimmune pancreatitis (AIP) is a unique form of chronic pancreatitis with a multifactorial pathogenesis. Historically, it has been classified as type 1 and type 2, according to its clinical and histological features. The diagnosis of AIP is challenging and relies on a combination of clinical, histopathologic, serologic, and imaging characteristics. In the available guidelines, the imaging hallmarks of AIP are based on cross-sectional imaging and cholangiopancreatography retrograde endoscopic findings. Endoscopic ultrasound (EUS) is generally used for pancreatic tissue acquisition to rule out pancreatic cancer and diagnose AIP with limited accuracy. Several papers reported the reliability of EUS for providing informative morphologic features of AIP. Nowadays, the improvement in the resolution of EUS conventional images and the development of new ancillary technologies have further increased the diagnostic yield of EUS: contrast-enhanced EUS and EUS elastography are non-invasive and real-time techniques that strongly support the diagnosis and management of pancreatic diseases. In this review article, we will present the role of conventional EUS and ancillary diagnostic techniques in the diagnosis of AIP to support clinicians and endosonographers in managing this condition.

Autoimmune pancreatitis: Cornerstones and future perspectives

Autoimmune pancreatitis (AIP) is an autoimmune subtype of chronic pancreatitis resulting from the aberrant immune response against the pancreas, leading to inflammation and fibrosis. Although AIP is rare, its incidence is increasing and is often misdiagnosed as other pancreatic diseases. AIP is commonly classified into two types. Type 1 AIP (AIP-1) is typically associated with elevated serum immunoglobulin G4 (IgG4) levels and systemic manifestations, while type 2 AIP is typically a more localized form of the disease, and may coexist with other autoimmune disorders, especially inflammatory bowel diseases. Additionally, there is emerging recognition of a third type (type 3 AIP), which refers to immunotherapy-triggered AIP, although this classification is still gaining acceptance in medical literature. The clinical manifestations of AIP mainly include painless jaundice and weight loss. Elevated serum IgG4 levels are particularly characteristic of AIP-1. Diagnosis relies on a combination of clinical, laboratory, radiological, and histological findings, given the similarity of AIP symptoms to other pancreatic disorders. The mainstay of treatment for AIP is steroid therapy, which is effective in most cases. Severe cases might require additional imm-unosuppressive agents. This review aims to summarize the current knowledge of AIP, encompassing its epidemiology, etiology, clinical presentation, diagnosis, and treatment options. We also address the challenges and controversies in diagnosing and treating AIP, such as distinguishing it from pancreatic cancer and managing long-term treatment, highlighting the need for increased awareness and knowledge of this complex disease.

Quantitative Magnetic Resonance Imaging for the Pancreas: Current Status

ABSTRACT

Magnetic resonance imaging (MRI) is important for evaluating pancreatic disorders, and anatomical landmarks play a major role in the interpretation of results. Quantitative MRI is an effective diagnostic modality for various pathologic conditions, as it allows the investigation of various physical parameters. Recent advancements in quantitative MRI techniques have significantly improved the accuracy of pancreatic MRI. Consequently, this method has become an essential tool for the diagnosis, treatment, and monitoring of pancreatic diseases. This comprehensive review article presents the currently available evidence on the clinical utility of quantitative MRI of the pancreas.

Differentiation of autoimmune pancreatitis from pancreatic adenocarcinoma using CT characteristics: a systematic review and meta-analysis

OBJECTIVES

To determine informational CT findings for distinguishing autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC) and to review their diagnostic accuracy.

METHODS

A systematic and detailed literature review was performed through PubMed, EMBASE, and the Cochrane library. Similar descriptors to embody the identical image finding were labeled as a single CT characteristic. We calculated the pooled diagnostic odds ratios (DORs) of each CT characteristic using a bivariate random-effects model.

RESULTS

A total of 145 various descriptors from 15 studies (including 562 AIP and 869 PDAC patients) were categorized into 16 CT characteristics. According to the pooled DOR, 16 CT characteristics were classified into three groups (suggesting AIP, suggesting PDAC, and not informational). Seven characteristics suggesting AIP were diffuse pancreatic enlargement (DOR, 48), delayed homogeneous enhancement (DOR, 46), capsule-like rim (DOR, 34), multiple pancreatic masses (DOR, 16), renal involvement (DOR, 15), retroperitoneal fibrosis (DOR, 13), and bile duct involvement (DOR, 8). Delayed homogeneous enhancement showed a pooled sensitivity of 83% and specificity of 85%. The other six characteristics showed relatively low sensitivity (12-63%) but high specificity (93-99%). Four characteristics suggesting PDAC were discrete pancreatic mass (DOR, 23), pancreatic duct cutoff (DOR, 16), upstream main pancreatic duct dilatation (DOR, 8), and upstream parenchymal atrophy (DOR, 7).

CONCLUSION

Eleven CT characteristics were informational to distinguish AIP from PDAC. Diffuse pancreatic enlargement, delayed homogeneous enhancement, and capsule-like rim suggested AIP with the highest DORs, whereas discrete pancreatic mass suggested PDAC. However, pooled sensitivities of informational CT characteristics were moderate.

CLINICAL RELEVANCE STATEMENT

This meta-analysis underscores eleven distinctive CT characteristics that aid in differentiating autoimmune pancreatitis from pancreatic adenocarcinoma, potentially preventing misdiagnoses in patients presenting with focal/diffuse pancreatic enlargement.

KEY POINTS

• Diffuse pancreatic enlargement (pooled diagnostic odds ratio [DOR], 48), delayed homogeneous enhancement (46), and capsule-like rim (34) were CT characteristics suggesting autoimmune pancreatitis. • The CT characteristics suggesting autoimmune pancreatitis, except delayed homogeneous enhancement, had a general tendency to show relatively low sensitivity (12-63%) but high specificity (93-99%). • Discrete pancreatic mass (pooled diagnostic odds ratio, 23) was the CT characteristic suggesting pancreatic ductal adenocarcinoma with the highest pooled DORs.

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.

Challenges for clinicians treating autoimmune pancreatitis: Current perspectives

Autoimmune pancreatitis (AIP) is a rare disease clinically characterized by obstructive jaundice, unintentional weight loss, acute pancreatitis, focal pancreatic mass, and diabetes. AIP is classified into two subtypes - type 1 and type 2 - according to pathological findings, clinical features, and serology test results, but some cases may be defined as type not otherwise in the absence of pathological findings and inflammatory bowel disease. To address the differences in diagnostic criteria by country, standard diagnostic criteria for AIP were proposed in 2011 by an international consensus of expert opinions. Differential diagnosis of AIP from pancreatic ductal adenocarcinoma is important but remains challenging for clinicians. Fortunately, all subtypes of AIP show dramatic response to steroid treatment. This review discusses the current perspectives on the diagnosis and management of AIP in clinical practice.

A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma

Objectives

The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.

Methods

96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.

Results

A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.

Conclusions

The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma

OBJECTIVE

To evaluate the parameters of support vector machine (SVM) using imaging data generated from the apparent diffusion coefficient (ADC) to differentiate between focal-type autoimmune pancreatitis (f-AIP) and pancreatic ductal adenocarcinoma (PDAC) when using SVM based on diffusion-weighted imaging.

METHODS

The 2D-ADCmean and texture parameters (16 texture features × [non-filter+17 filters]) were retrospectively segmented by 2 readers in 28 patients with f-AIP and 77 patients with pathologically proven PDAC. The diagnostic accuracy of the SVM model was evaluated by receiver operating characteristic curve analysis and calculation of the area under the curve (AUC). Interreader reliability was assessed by intraclass correlation coefficient (ICC).

RESULTS

The 2D-ADCmean and 3D-ADCmean were significantly lower in cases of f-AIP (1.10-1.15 × 10-3 mm2/s and 1.21-1.23× 10-3 mm2/s, respectively) vs PDAC (1.29-1.33 × 10-3 mm2/s and 1.41-1.43 × 10-3 mm2/s, respectively), with excellent and good interreader reliability, respectively (ICC = 0.909 and 0.891, respectively). Among the texture parameters, energy with exponential filtering yielded the highest AUC (Reader 1: 74.7%, Reader 2: 81.5%), with fair interreader reliability (ICC = 0.707). The non-linear SVM, a combination of 2D-ADCmean, object volume and exponential-energy showed an AUC value of 96.2% in the testing cohorts.

CONCLUSION

Our results suggest that non-linear SVM using a combination of 2D-ADCmean, object volume, and exponential-energy may assist in differentiating f-AIP from PDAC.

ADVANCES IN KNOWLEDGE

The radiomics based on an apparent diffusion coefficient value may assist in differentiating f-AIP from PDAC.

Steroid treatment response combined with serological mark in differentiating type-1 autoimmune pancreatitis from pancreatic cancer

Autoimmune pancreatitis (AIP) and pancreatic cancer (PC) are two different diseases. Their diagnosis, treatment, and prognosis are different, and it is difficult to differentiate them. This study aimed to explore the role of steroid treatment response combined with serological mark in distinguishing type-1 AIP from PC. Clinical data were collected and compared from 50 cases of AIP (group 1) and 100 cases of PC (group 2). The diagnostic value of serum IgG4, CA19-9, globulin, and eosinophil cell (EC) were evaluated. The response of steroid treatment of 28 patients with atypical imaging in group 1 was analyzed. After 2 weeks, the patients were classified as positive and negative steroid response according to the manifestations and/or the radiological changes. The positive response cases (n = 20) were confirmed as AIP, whereas negative ones (n = 8) were finally diagnosed as PC after complete resection. Serum globulin, IgG4, and EC levels in group 1 were significantly higher than those in group 2 (P < .01), and CA19-9 levels were distinctly lower in group 1 (P < .01). The level of serum IgG4 was related to the accuracy of diagnosis of AIP on the basis of the result of logistic regression analysis. Two-weeks steroid therapy response combined with serum IgG4 levels contribute to the differential diagnosis AIP and PC. However, regular and long-term follow-up were important for the differential diagnosis. There was an urgent need to explore the specific markers that distinguish these 2 entities.

Systematic review and meta-analysis of MRI features for differentiating autoimmune pancreatitis from pancreatic adenocarcinoma

OBJECTIVES

To identify reliable MRI features for differentiating autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC) and to summarize their diagnostic accuracy.

METHODS

We conducted a systematic literature review and meta-analysis using PubMed, EMBASE, and the Cochrane Library to identify original articles published between January 2006 and July 2021. The pooled diagnostic accuracy, including the diagnostic odds ratios (DORs) with 95% confidence intervals (CIs) of the identified features, was calculated using a bivariate random effects model.

RESULTS

Twelve studies were included, and 92 overlapping descriptors were subsumed under 16 MRI features. Ten features favoring AIP were diffuse enlargement (DOR, 75; 95% CI, 9-594), capsule-like rim (DOR, 52; 95% CI, 20-131), multiple main pancreatic duct (MPD) strictures (DOR, 47; 95% CI, 17-129), homogeneous delayed enhancement (DOR, 46; 95% CI, 21-104), low apparent diffusion coefficient value (DOR, 30), speckled enhancement (DOR, 30), multiple pancreatic masses (DOR, 29), tapered narrowing of MPD (DOR, 15), penetrating duct sign (DOR, 14), and delayed enhancement (DOR, 13). Six features favoring PDAC were target type enhancement (DOR, 41; 95% CI, 11-158), discrete pancreatic mass (DOR, 35; 95% CI, 15-80), upstream MPD dilatation (DOR, 13), peripancreatic fat infiltration (DOR, 10), upstream parenchymal atrophy (DOR, 5), and vascular involvement (DOR, 3).

CONCLUSION

This study identified 16 informative MRI features to differentiate AIP from PDAC. Among them, diffuse enlargement, capsule-like rim, multiple MPD strictures, and homogeneous delayed enhancement favored AIP with the highest DORs, whereas discrete mass and target type enhancement favored PDAC.

KEY POINTS

• The MRI features with the highest pooled diagnostic odds ratios (DORs) for autoimmune pancreatitis were diffuse enlargement of the pancreas (75), capsule-like rim (52), multiple strictures of the main pancreatic duct (47), and homogeneous delayed enhancement (46). • The MRI features with the highest pooled DORs for pancreatic ductal adenocarcinoma were target type enhancement (41) and discrete pancreatic mass (35).

Diffuse Involvement of Pancreas is not Always Autoimmune Pancreatitis

RATIONALE AND OBJECTIVES

To determine the prevalence of diffuse involvement of pancreas and to identify the findings of malignancies using enhancement computed tomography (CT).

MATERIALS AND METHODS

A total of 1,0249 patients performed enhancement CT in our hospital over 62 months were investigated and the final study cohort includes 245 patients (170 males, 75 females; mean age, 56.94 ± 12.17 years). The reference standard is the final clinical/pathological diagnosis. The lesion-to-aorta enhancement ratio (LAR) on the pancreatic arterial phase, portal phase and delayed phase (DP) and the traditional CT findings were evaluated. Intergroup comparisons between malignancies and non-malignancies lesions were performed. Univariate and multivariate analyses were conducted to identify findings predicting malignancies.

RESULTS

The prevalence of malignancy was 45.3% (111/245) of diffuse enlargement of pancreas. All benign lesions were autoimmune pancreatitis 54.7% (n = 134). The most common malignant lesion was pancreatic ductal adenocarcinoma (n = 88, 35.9%). Other rare lesions with malignant potential included pancreatic neuroendocrine tumor (n = 11, 4.5%), lymphoma (n = 4, 1.6%), metastasis (n = 4, 1.6%), solid pseudopapillary neoplasm (n = 3, 1.2%) and acinar cell carcinoma (n = 1, 0.4%). Residual normal pancreas parenchyma, heterogeneity, short axis (cut-off value, 3.15 cm) and LARDP (cut-off value, 0.75) were independent predictors of malignancies. When the above predictors were combined, a sensitivity of 94.2%, a specificity of 90.8% were attained.

CONCLUSION

Diffuse involvement of the pancreas is rare and is not a specific sign of autoimmune pancreatitis, and it is associated with a wide spectrum of malignant conditions. Dynamic enhancement CT is helpful to identifying malignancies.

Radiomics in pancreatic cancer for oncologist: Present and future

Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.

MRI features for differentiation of autoimmune pancreatitis from pancreatic ductal adenocarcinoma: A systematic review and meta-analysis

BACKGROUND AND AIMS

The accurate differential diagnosis between autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) is clinically important. We aimed to determine significant MRI features for differentiating AIP from PDAC, including assessment of diffusion-weighted imaging (DWI).

METHODS

We performed a systematic search using three databases. The pooled diagnostic odds ratio was calculated using a bivariate random effects model to determine significant MRI features for differentiating AIP from PDAC. The pooled sensitivity and specificity were calculated. The qualitative systematic review for DWI assessment was performed.

RESULTS

Of nine studies (775 patients), multiple main pancreatic duct (MPD) strictures, absence of upstream marked MPD dilatation, peripancreatic rim, and duct penetration sign were significant MRI features for differentiating AIP from PDAC. Absence of MPD dilatation had the highest pooled sensitivity (87%, 95% CI=68-96%), whereas peripancreatic rim had the highest pooled specificity (100%, 95% CI=88-100%). Of 12 studies evaluating DWI, seven reported statistically significant differences in apparent diffusion coefficient (ADC) values between AIP and PDAC; however, four reported lower ADC values in AIP than in PDAC, but three reported the opposite result.

CONCLUSION

The four significant MRI features can be useful to differentiate AIP from PDAC, but DWI assessment might be limited.

American College of Rheumatology and the European League Against Rheumatism classification criteria for IgG4-related disease: an update for radiologists

IgG4-related disease (IgG4-RD) is an immune-mediated, multiorgan, chronic inflammatory disease. The three-step classification criteria proposed in 2019 by the American College of Rheumatology and the European League Against Rheumatism (ACR/EULAR) encompass a wide range of clinical, radiological, serological, and histopathological findings. The ACR/EULAR proposed a three-step classification process, i.e., entry step, exclusion step, and scoring system. Radiologists need to know that the radiological findings observed in the five domains of the lacrimal and salivary glands, chest, pancreas and biliary ducts, kidney, and retroperitoneum are independently weighted with different points in the scoring system. A total score < 20 points indicates that the patient should not be classified as having IgG4-RD; conversely, a total score ≥ 20 points indicates that the patient should be classified as having IgG4-RD. In this review, the 2019 ACR/EULAR classification criteria are discussed, focusing on the interpretation of each radiological item, with the aim of applying them to the diagnosis of IgG4-RD in clinical practice.

CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis

PURPOSE

To evaluate the diagnostic performance of the radiomics score (rad-score) for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria between January 2011 and December 2018. Furthermore, 334 consecutive patients with PDAC confirmed by pathology were also reviewed during the same period. Patients with PDAC and fAIP were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, 1409 radiomics features of the portal phase were extracted and reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The portal rad-score performance was assessed based on its discriminative ability.

RESULTS

After PSM, we matched 55 patients with PDAC to 42 patients with fAIP, based on clinical and CT characteristics (e.g., patient age, sex, body mass index, location, size, enhanced mode). A rad-score for discriminating fAIP from PDAC, which contained four CT derived radiomic features, was developed (area under the curve = 0.97). The sensitivity, specificity, and accuracy of the radiomics model were 95.24%, 92.73% and 0.94, respectively.

CONCLUSION

The portal rad-score can accurately and noninvasively differentiate fAIP from PDAC.

Amendment of the Japanese consensus guidelines for autoimmune pancreatitis, 2020

In response to the latest knowledge and the amendment of the Japanese diagnostic criteria for autoimmune pancreatitis (AIP) in 2018, the Japanese consensus guidelines for managing AIP in 2013 were required to be revised. Three committees [the professional committee for developing clinical questions (CQs) and statements by Japanese specialists; the expert panelist committee for rating statements by the modified Delphi method; and the evaluating committee of moderators] were organized. Twenty specialists in AIP extracted the specific clinical statements from a total of 5218 articles (1963-2019) from a search in PubMed and the Cochrane Library. The professional committee made 14, 9, 5, and 11 CQs and statements for the current concept and diagnosis, extra-pancreatic lesions, differential diagnosis, and treatment, respectively. The expert panelists regarded the statements as valid after a two-round modified Delphi approach with individually rating these clinical statements, in which a clinical statement receiving a median score greater than 7 on a 9-point scale from the panel was regarded as valid. After evaluation by the moderators, the amendment of the Japanese consensus guidelines for AIP has been proposed in 2020.

Use of MRI signal intensity ratio to differentiate between autoimmune pancreatitis and pancreatic ductal adenocarcinoma

AIM

To evaluate the accuracy of the lesion-to-erector spinae signal intensity ratio (SIR) on magnetic resonance imaging (MRI) for distinguishing autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDA).

MATERIALS AND METHODS

The MRI data of 21 patients with AIP and 27 patients with PDA were analysed retrospectively, and the signal intensity in pancreatic lesions and erector spinae muscles at the same level on T2-weighted imaging (T2WI), arterial phase (AP) imaging, and delayed phase (DP) imaging was measured for calculation of SIRs.

RESULTS

The mean SIRs of the pancreatic lesions and erector spinae from T2WI, AP, and DP images of AIP patients were 0.96, 1.27, and 1.42, respectively, while those of PDA patients were 1.35, 0.80, and 0.91, respectively. The differences in the SIRs between the AIP and PDA groups were statistically significant (p<0.001), with corresponding area under curve (AUC) values of 0.925, 0.906, and 0.961, respectively. The optimal cut-off values for the SIRs on T2WI, AP and DP images were 1.21, 1.01, and 1.08, respectively. SIR values < 1.21 on T2WI, >1.01 on AP imaging, and >1.08 on DP imaging identified AIP with sensitivities of 85.7%, 90.5%, and 90.5%, respectively, and specificities of 81.5%, 74.6%, and 81.5%, respectively. The AUC values for SIRs did not differ significantly between T2WI and DP imaging or AP and DP imaging (Z = 0.778, p=0.436; Z = 1.279, p=0.201).

CONCLUSION

The SIRs of pancreatic lesions and erector spinae on T2WI, AP, and DP images can be used to differentiate AIP from PDA.

Multimodel magnetic resonance imaging of mass-forming autoimmune pancreatitis: differential diagnosis with pancreatic ductal adenocarcinoma

PURPOSE

To assess the value of the multimodel magnetic resonance imaging (MRI), including unenhanced images, dynamic contrast-enhanced MRI (DCE-MRI), MR-cholangiopancreatography (MRCP), and diffusion-weighted imaging (DWI), in differentiation of mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

Twelve patients with mass-forming AIP and 30 with PDAC were included. All patients underwent unenhanced MRI, DCE-MRI, DWI, and MRCP. Relevant values including sensitivity and specificity of the imaging features and their diagnostic performance for predicting mass-forming AIP were analyzed.

RESULTS

Several statistically significant MR findings and quantitative indexes differentiating mass-forming AIP from PDAC, including multiplicity, irregularity or conformation, capsule-like rim enhancement, absence of internal cystic or necrotic portion, homogeneous enhancement during pancreatic, venous, and delayed phases, skipped stricture or stricture of MPD, absence of side branch dilation, maximum upstream MPD diameter < 2.4 mm, ContrastUP > 0.739, ContrastAP > 0.710, ContrastPP > 0.879, and ContrastVP or ContrastDP > 0.949, indicated mass-forming AIP (P < 0.05). The apparent diffusion coefficient (ADC) value was also significantly lower in mass-forming AIP compared to that in PDAC (P = 0.006). The cutoff value of ADC for distinguishing mass-forming AIP from PDAC was 1.099 × 10-3 mm2/s.

CONCLUSION

Multimodel MRI, including unenhanced MRI, DCE-MRI with DWI and MRCP can provide qualitative and quantitative information about mass-forming AIP characterization. Multimodel MRI are valuable for differentiating mass-forming AIP from PDAC.

Korean clinical practice guideline for pancreatic cancer 2021: A summary of evidence-based, multi-disciplinary diagnostic and therapeutic approaches

Pancreatic cancer is the eighth most common cancer and the fifth most common cause of cancer-related death in Korea. To enable standardization of management and facilitate improvements in outcome, a total of 53 multi-disciplinary experts in gastroenterology, surgery, medical oncology, radiation oncology, radiology, nuclear medicine, and pathology in Korea developed new recommendations that integrate the most up-to-date, evidence-based research findings and expert opinions. Recommendations were made on imaging diagnosis, endoscopic management, surgery, radiotherapy, palliative chemotherapy, and specific management procedures, including neoadjuvant treatment or adjuvant treatment for patients with resectable, borderline resectable, and locally advanced unresectable pancreatic cancer. This is the English version of the Korean clinical practice guideline for pancreatic cancer 2021. This guideline includes 20 clinical questions and 32 statements. This guideline represents the most standard guideline for the diagnosis and treatment of patients with pancreatic ductal adenocarcinoma in adults at this time in Korea. The authors believe that this guideline will provide useful and informative advice.

Iodine concentration and tissue attenuation in dual-energy contrast-enhanced CT as a potential quantitative parameter in early detection of local pancreatic carcinoma recurrence after surgical resection

PURPOSE

Due to the difficult differentiation from non-specific postoperative soft tissue formation (PSF), early diagnosis of pancreatic carcinoma recurrence remains challenging. Thus, we investigated the diagnostic potential of dual-energy (DE) contrast-enhanced CT.

METHOD

After potentially curative pancreatic carcinoma resection, 31 consecutive patients with PSF were examined via DE perfusion CT, acquiring 34 images (80 kVp/140 kVp) every 1.5 s, as the initial purpose of this study was evaluating CT-Perfusion. Corresponding time points of arterial, pancreatic, and early venous phase were calculated from bolus trigger times in prior conventional CT. Iodine and 120 kVp-equivalent images were calculated. Regions of interest were placed in each soft tissue formation. Diagnosis of local recurrence was confirmed by regular follow-up or histopathology.

RESULTS

Final diagnosis was local recurrence in 17 patients and non-specific PSF in 14 patients. Iodine concentrations in early venous phase were significantly higher in recurrent carcinoma than in non-specific PSF (1.47 mg/ml vs. 0.96 mg/ml, p = 0.007). In earlier contrast phases iodine concentrations tended to be higher, but not significantly. CT numbers in recurrent carcinoma in 120 kVp-equivalent images in venous phase were significantly higher, too (74HU vs 47HU, p = 0.002). ROC-curve analysis for iodine concentrations in early venous phase suggests a cut-off value of ≥ 1.55 mg/ml for local recurrence (AUC = 0.78, specificity = 1.0, sensitivity = 0.53) and for CT numbers in 120kVp-equivalent images a cut-off value of ≥ 57HU (AUC = 0.82, specificity = 0.82, sensitivity = 0.71).

CONCLUSION

In difficult cases, measuring iodine concentrations or CT numbers in PSF in (early) venous phase DECT could be a valuable additional parameter for differentiating local recurrence from non-specific PSF.

Multifocal autoimmune pancreatitis: A retrospective study in a single tertiary center of 26 patients with a 20-year literature review

BACKGROUND

Multifocal-type autoimmune pancreatitis (AIP), sometimes forming multiple pancreatic masses, is frequently misdiagnosed as pancreatic malignancy in routine clinical practice. It is critical to know the imaging features of multifocal-type AIP to prevent misdiagnosis and unnecessary surgery. To the best of our knowledge, there have been no studies evaluating the value of diffusionweighted imaging (DWI), axial fat-suppressed T1 weighted image (T1WI), and dynamic contrast enhanced-computed tomography (DCE-CT) in detecting the lesions of multifocal-type AIP.

AIM

To clarify the exact prevalence and radiological findings of multifocal AIP in our cohorts and compare the sensitivity of DWI, axial fat-suppressed T1WI, and DCE-CT for detecting AIP lesions. We also compared radiological features between multifocal AIP and pancreatic ductal adenocarcinoma with several key imaging landmarks.

METHODS

Twenty-six patients with proven multifocal AIP were retrospectively included. Two blinded independent radiologists rated their confidence level in detecting the lesions on a 5-point scale and assessed the diagnostic performance of DWI, axial fat-suppressed T1WI, and DCE-CT. CT and magnetic resonance imaging of multifocal AIP were systematically reviewed for typical imaging findings and compared with the key imaging features of pancreatic ductal adenocarcinoma.

RESULTS

Among 118 patients with AIP, 26 (22.0%) had multiple lesions (56 lesions). Ulcerative colitis was associated with multifocal AIP in 7.7% (2/26) of patients, and Crohn’s disease was present in 15.3% (4/26) of patients. In multifocal AIP, multiple lesions, delayed homogeneous enhancement, multifocal strictures of the main pancreatic duct, capsule-like rim, lower apparent diffusion coefficient values, and elevated serum Ig4 level were observed significantly more frequently than pancreatic ductal adenocarcinoma, whereas the presence of capsule-like rim in multifocal-type AIP was lower in frequency than total AIP. Of these lesions of multifocal AIP, DWI detected 89.3% (50/56) and 82.1% (46/56) by the senior and junior radiologist, respectively.

CONCLUSION

Multifocal AIP is not as rare as previously thought and was seen in 22.0% of our patients. The diagnostic performance of DWI for detecting multifocal AIP was best followed by axial fat-suppressed T1WI and DCE-CT.

The role of apparent diffusion coefficient value in the diagnosis of localized type 1 autoimmune pancreatitis: differentiation from pancreatic ductal adenocarcinoma and evaluation of response to steroids

PURPOSE

The aim of this study was to clarify the usefulness of the apparent diffusion coefficient (ADC) value in the differential diagnosis of localized autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) and the evaluation of response to steroids.

METHODS

This study retrospectively analyzed 40 patients with localized AIP and 71 patients with PDAC who underwent abdominal MRI with DWI (b = 0 and 1000 s/mm²). Their ADC values at the lesion sites and five MRI findings useful for diagnosing AIP were evaluated. In addition, ADC values before and after steroid therapy were compared in 28 patients with localized AIP.

RESULTS

The median ADC value was significantly lower for localized AIP than for PDAC (1.057 × 10^-3 vs 1.376 × 10^-3 mm²/s, P < 0.001). In the ROC curve analysis, the area under the curve was 0.957 and optimal cut-off value of ADC for differentiating localized AIP from PDAC was 1.188 × 10^-3 mm²/s. ADC value ≤ 1.188 × 10^-3 mm²/s showed the highest sensitivity and accuracy among the MRI findings (92.6% and 90.7%, respectively), and when combined with one or more other MRI findings, showed 96.3% specificity. The median ADC values before and after steroid therapy (mean 7.9 days) were 1.061 × 10^-3 and 1.340 × 10^-3 mm²/s, respectively, and ADC values were significantly elevated after steroid induction (P < 0.001).

CONCLUSION

The measurement of ADC values was useful for the differential diagnosis of localized AIP and PDAC and for the early determination of the effect of steroid therapy.

Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

Artificial intelligence (AI), particularly the deep learning technology, have been proven influential to radiology in the recent decade. Its ability in image classification, segmentation, detection and reconstruction tasks have substantially assisted diagnostic radiology, and has even been viewed as having the potential to perform better than radiologists in some tasks. Gastrointestinal radiology, an important subspecialty dealing with complex anatomy and various modalities including endoscopy, have especially attracted the attention of AI researchers and engineers worldwide. Consequently, recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology, particularly in the fields for which public databases are available, such as diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology, with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT. For fields where AI is less developed, this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues. The authors’ insights of possible future development will be addressed in the last section.

Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis

BACKGROUND

A variety of imaging techniques can be used to evaluate diffusion characteristics to differentiate malignant and benign pancreatic lesions. The diagnostic performance of diffusion parameters has not been systematic assessed.

PURPOSE

We aimed to investigate the diagnostic efficacy of quantitative diffusion-weighted imaging (DWI) for pancreatic lesions.

METHODS

A literature search was conducted using the PubMed, Embase, and Cochrane Library databases for studies from inception to March 30, 2020, which involves the quantitative diagnostic performance of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in the pancreas. Studies were reviewed according to inclusion and exclusion criteria. The quality of articles was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUATAS-2). A bivariate random-effects model was used to evaluate pooled sensitivities and specificities. Univariable meta-regression analysis was used to test the effects of factors that contributed to the heterogeneity.

RESULTS

A total of 31 studies involving 1558 patients were ultimately eligible for data extraction. The lowest heterogeneity was found in specificity of perfusion fraction (f) with the I² value was 17.97% and Cochran p value was 0.28. However, high heterogeneities were found for the other parameters (all I² > 50%). There was no publication bias found in funnel plot (p = 0.30) for the apparent diffusion coefficient (ADC) parameter. The pooled sensitivities for ADC, f, pure diffusion coefficient (D), and pseudo diffusivity coefficient (D*) were 83%, 81%, 76%, and 84%, respectively. The pooled specificities for ADC, f, D, and D* were 87%, 83%, 69%, and 81% respectively. The areas under the curves for ADC, f, D, and D* were 0.92, 0.87, 0.79, and 0.87 respectively.

CONCLUSION

Quantitative DWI and IVIM have a good diagnostic performance for differentiating malignant and benign pancreatic lesions.

KEY POINTS

• IVIM has high sensitivity and specificity (84% and 83%, respectively) for differential diagnosis of pancreatic lesions, which is comparable to that of the ADC (83% and 87%, respectively). • The ADC has an excellent diagnostic performance for differentiating malignant from benign IPMNs (sensitivity, 0.83; specificity, 0.92); the f has the best diagnostic performance for differentiating pancreatic carcinoma from PNET (sensitivity, 0.85; specificity, 0.85). • For the ADC, using a maximal b value < 800 s/mm² has a higher diagnostic accuracy than ≥ 800 s/mm²; performing in a high field strength (3.0 T) system has a higher diagnostic accuracy than a low field strength (1.5 T) for pancreatic lesions.

Comparison of five-phase computed tomography images of type 1 autoimmune pancreatitis and pancreatic cancer: Emphasis on cases with atypical images

BACKGROUND/OBJECTIVES

International consensus diagnostic criteria (ICDC) include characteristic images of autoimmune pancreatitis (AIP); however, reports on atypical cases are increasing. The aims of this study were to compare CT findings between AIP and pancreatic cancer (PC), and to analyze type 1 AIPs showing atypical images.

METHODS

Five-phase CT images were compared between 80 type 1-AIP lesions and 80 size- and location-matched PCs in the case-control study. Atypical AIPs were diagnosed based on the four ICDC items.

RESULTS

ICDC items were recognized in most AIP lesions; pancreatic enlargement (87.7%), narrowing of the main pancreatic duct (98.8%), delayed enhancement (100%), and no marked upstream-duct dilation (97.5%). CT values of AIPs increased rapidly until the pancreatic phase and decreased afterward, while those of PCs gradually increased until the delayed phase (P < 0.0001). Atypical images were recognized in 14.8% of AIPs, commonly without pancreatic enlargement (18.5 mm) and sometimes mimicking intraductal neoplasms. The CT values and their ratios were different between atypical AIPs and size-matched PCs most significantly in the pancreatic phase, but similar in the delayed phase.

CONCLUSIONS

Ordinary type 1 AIPs can be diagnosed with the ICDC, but atypical AIPs represented a small fraction. "Delayed enhancement" is characteristic to ordinary AIPs, however, "pancreatic-phase enhancement" is more diagnostic for atypical AIPs.

Effective apparent diffusion coefficient parameters for differentiation between mass-forming autoimmune pancreatitis and pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) parameters by region of interest (ROI) methods in differentiating mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

The institutional review board approved this retrospective study and the requirement for informed consent was waived. Twenty-three patients with mass-forming AIP and 144 patients with PDAC underwent diffusion-weighted imaging with b-values of 0 s/mm² and 800 s/mm². The minimum, maximum, and mean ADC values obtained by placing ROIs within lesions and percentile ADC values (10th, 25th, 50th, 75th, and 90th) from entire-lesion histogram analysis were compared between the two groups by using Mann-Whitney U tests. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

The minimum, maximum, and mean ADC values were significantly different between mass-forming AIP and PDAC groups. ROC curve analysis showed that the maximum ADC had the highest diagnostic performance (0.92), while the minimum ADC value had the lowest diagnostic performance (0.72). The AUC of minimum ADC was significantly lower than that of maximum or mean ADC (P < 0.0001, P < 0.0001). The AUC was lowest in 10th percentile ADC value and highest in 90th percentile value. The AUC increased along with the increase of percentile values.

CONCLUSION

Either the maximum or mean ADC value was effective in differentiating mass-forming AIP from the PDAC group, while the minimum ADC value might not be recommended.

Diagnostic performance of diffusion MRI for pancreatic ductal adenocarcinoma characterisation: A meta-analysis

PURPOSE

To assess the diagnostic performance of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) for characterising pancreatic ductal adenocarcinoma (PDAC).

METHOD

A literature search was performed through PubMed, Web of Science, the Cochrane Library, and Embase databases. The search date was updated to extend until 28 October 2020, with no starting time limitation. The pooled sensitivity and specificity were calculated using a bivariate random effects model. Summary receiver operating characteristic curves were constructed, and area under the curve (AUC) of each diffusion parameter was calculated. Subgroup and meta-regression analyses were performed to assess for heterogeneity. Study quality was assessed.

RESULTS

Twenty-nine studies involving 1579 participants were included, of which 26 evaluated the apparent diffusion coefficient (ADC) and eight evaluated IVIM, with five evaluating both ADC and IVIM. Pooled sensitivity and specificity of ADC were 83 % (95 % CI, 76 %-88 %, I² = 86 %) and 85 % (95 % CI, 79 %-90 %, I² = 77 %), respectively, and AUC was 0.91 (95 % CI, 0.88-0.93). The perfusion fraction had the highest diagnostic accuracy in the IVIM model; the pooled sensitivity, specificity, and AUC were 87 % (95 % CI, 81 %-92 %, I² = 45 %), 88 % (95 % CI, 77 %-94 %, I² = 57 %), and 0.93 (95 % CI, 0.91-0.95), respectively. The pooled sensitivity, specificity and AUC for the tissue diffusion coefficient were 74 % (95 % CI, 55 %-87 %, I² = 87 %), 69 % (95 % CI, 52 %-82 %, I² = 73 %), and 0.77 (95 % CI, 0.73-0.81), respectively. And the pooled sensitivity, specificity, and AUC for the pseudodiffusion coefficient were 89 % (95 % CI, 77 %-96 %, I² = 79 %), 74 % (95 % CI, 60 %-84 %, I² = 78 %), and 0.88(95 %CI,0.85-0.91), respectively. Meta-regression analyses revealed that study design (specificity, P＜0.01), region-of-interest delineation (sensitivity, P = 0.02;specificity, P = 0.03), field strength (sensitivity, P＜0.01), and thickness (sensitivity, P＜0.01; specificity, P = 0.01) were sources of ADC heterogeneity.

CONCLUSIONS

DWI and IVIM have comparable diagnostic power and good diagnostic performance for characterising PDAC.

Mass Mimicking Autoimmune Pancreatitis—A Report of Two Cases and Review of Literature

Autoimmune pancreatitis (AIP) consists of two clinically histologically distinct forms (type I and II) of chronic pancreatitis that are histologically different. These forms of AIP classically respond to oral steroids. The focal form of AIP resembles pancreatic carcinoma both clinically and radiologically and it is of utmost importance to make an early correct diagnosis between these two diseases in order to identify the optimal therapeutic strategy and to avoid unnecessary laparotomy or pancreatic resection in AIP patients. Here we report focal forms of type I and II AIP with clinical and imaging features closely mimicking pancreatic carcinoma.

Optimal target b-value on computed diffusion-weighted magnetic resonance imaging for visualization of pancreatic ductal adenocarcinoma and focal autoimmune pancreatitis

PURPOSE

To compare computed diffusion-weighted imaging (cDWI) feasibility with that of directly acquired DWI for visualizing pancreatic ductal adenocarcinoma (PDAC) and focal autoimmune pancreatitis (AIP).

METHODS

From April 2012 to January 2017, 135 patients with PDAC (n = 111) or focal AIP (n = 24) were retrospectively enrolled. They underwent DWI with b-values of 0, 500, and 1000 s/mm². From DWI₀ and DWI₁₀₀₀, we generated cDWIs with targeted b-values of 1500, 2000, and 3000 s/mm². The lesions' signal intensities, image quality, signal intensity ratio (SIR) of lesions and pancreatic parenchyma to spinal cord, and lesion-to-pancreatic parenchyma contrast ratio (CR) were compared among the five DWI protocols (DWI₅₀₀, DWI₁₀₀₀, cDWI₁₅₀₀, cDWI₂₀₀₀, and cDWI₃₀₀₀). SIR was analyzed by receiver operating characteristic (ROC) analyses.

RESULTS

DWI₅₀₀, DWI₁₀₀₀, and cDWI₁₅₀₀ had higher image quality than cDWI₂₀₀₀ and cDWI₃₀₀₀ (P < 0.001). The incidence of clear hyperintense PDAC was highest on cDWI₂₀₀₀, followed by cDWI₁₅₀₀, and cDWI₃₀₀₀ (P < 0.001-0.002), while the incidence of clear hyperintense AIP was higher on DWI₁₀₀₀, cDWI₁₅₀₀, and cDWI₂₀₀₀ than on DWI₅₀₀ and cDWI₃₀₀₀ (P = 0.001-0.022). SIRs decreased whereas CRs increased as the b-value increased, for both PDAC and AIP. The area under the ROC curve (AUC) of SIR_lesion was significantly lower on cDWI₁₅₀₀ than on cDWI₂₀₀₀ and cDWI₃₀₀₀ (P < 0.001).

CONCLUSION

cDWI₁₅₀₀ or cDWI₂₀₀₀ generated from b-values of 0 and 1000 s/mm² were the most effective for visualizing PDAC and focal AIP; however, the SIR_lesion AUC was significantly lower on cDWI₁₅₀₀ than on cDWI₂₀₀₀ and cDWI₃₀₀₀.

Imaging of IgG4-related disease in the extracranial head and neck

PURPOSE

Immunoglobulin G4-related disease (IgG4-RD) is a newly defined fibro-inflammatory multisystemic condition defined by a triad of diagnostic criteria based on clinical presentation, biochemical and histopathological findings. Whereas some subsites of this disease have been well described in the literature so far (e.g. pancreas, kidneys, retroperitoneum, salivary glands), more recently identified anatomical sites of involvement in the head and neck are less well understood (e.g. nose, paranasal sinuses).

METHOD

This pictorial review details the imaging appearances of extracranial IgG4-RD in the Head & Neck. Multimodality imaging appearance and features are presented, with reference to the published literature to date.

RESULTS

Following a subsite-based approach, we present both the most common and the more rarely encountered imaging patterns of IgG4-RD in the extracranial head and neck, along with the relevant differential diagnoses to consider. Our institutional experience not only cements what is already known in the existing literature on this topic, but also reveals new imaging features of IgG4-RD, notably in the sinonasal tract.

CONCLUSIONS

This pictorial review of extracranial head & neck IgG4-RD will enable radiologists to recognise the features of this condition and propose it as a differential diagnosis to include alongside other probable entities. It establishes the place of the radiologist in the diagnosis and management of IgG4-RD.

Systemic Manifestations of Immunoglobulin G4-Related Disease: A Pictorial Essay

Immunoglobulin G4 (IgG4)-related disease is a systemic fibro-inflammatory disease characterized by pathologic findings in various organs. Imaging is critical for the diagnosis and treatment assessment of patients with IgG4-related disease. In this pictorial essay, we review the key features of multiple imaging modalities, typical pathologic findings, and differential diagnosis of IgG4-related disease. This systematic pictorial review can further our understanding of the broad-spectrum manifestations of this disease.

The role of EUS in diagnosing focal autoimmune pancreatitis and differentiating it from pancreatic cancer

Background and Objectives:

The clinical presentation of focal autoimmune pancreatitis (FAIP) and together with radiological overlap can mimic pancreatic cancer (PC). The aim of this study is to construct and validate a prediction model for differentiating FAIP from PC according to EUS characteristics.

Patients and Methods:

Ninety patients with FAIP and 196 patients with PC, who consecutively underwent EUS at our center from January 2013 to December 2018, were retrospectively included in the study. The enrolled patients were randomly divided into either a derivation sample or a validation sample. According to EUS characteristics, multivariate stepwise logistic regression and receiver operating characteristics (ROC) analyses were used to construct a prediction model in derivation sample, and then, the efficacy was assessed in validation sample.

Results:

EUS characteristics that were suggestive of FAIP included diffuse hypoechogenicity, hyperechoic foci/stands or lobularity (parenchymal heterogeneity), bile duct wall thickening and peripancreatic hypoechoic margin; and EUS features favoring PC included focal hypoechogenicity, absence of parenchymal heterogeneity, pancreatic duct dilation, and vessel involvement. The prediction model, with an area under the ROC curve of more than 0.95, had a good capability to distinguish FAIP from PC. By using the optimal cutoff value, the efficacy of model for diagnosing PC showed 83.7%–91.8% sensitivity and 93.3%–95.6% specificity.

Conclusions:

It is feasible to differentiate FAIP from PC based on EUS characteristics. The prediction model built in this study needs to be further confirmed by multicenter prospective researches.

Imaging diagnosis of autoimmune pancreatitis: computed tomography and magnetic resonance imaging

Autoimmune pancreatitis (AIP) is a pancreatic phenotype of IgG4-related systemic disease. Since its first description in the literature, characteristic imaging features have gradually become known to many clinicians encompassing various specialties in the past quarter century. CT and MRI have been the workhorses for imaging diagnosis of AIP. Typical features include sausage-like swelling of the focal or entire pancreas, duct-penetrating sign, a capsule-like rim of the affected lesions, and homogeneous delayed enhancement or enhanced duct sign after contrast administration, as well as characteristic combined findings reflecting coexisting pathologies in the other organs as a systemic disease. In this review, recent and future developments in CT and MRI that may help diagnose AIP are discussed, including restricted diffusion and perfusion and increased elasticity measured using MR.

Meta-analysis of CT and MRI for differentiation of autoimmune pancreatitis from pancreatic adenocarcinoma

OBJECTIVES

To systematically determine the diagnostic performance of computed tomography (CT) and magnetic resonance imaging (MRI) for differentiating autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC), with a comparison between the two imaging modalities.

METHODS

Literature search was conducted using PubMed and EMBASE databases to identify original articles published between 2009 and 2019 reporting the diagnostic performance of CT and MRI for differentiating AIP from PDAC. The meta-analytic sensitivity and specificity of CT and MRI were calculated, and compared using a bivariate random effects model. Subgroup analysis for differentiating focal AIP from PDAC was performed.

RESULTS

Of the 856 articles screened, 11 eligible articles are remained, i.e., five studies for CT, four for MRI, and two for both. The meta-analytic summary sensitivity and specificity of CT were 59% (95% confidence interval [CI], 41-75%) and 99% (95% CI, 88-100%), respectively, while those of MRI were 84% (95% CI, 68-93%) and 97% (95% CI, 87-99%). MRI had a significantly higher meta-analytic summary sensitivity than CT (84% vs. 59%, p = 0.02) but a similar specificity (97% vs. 99%, p = 0.18). In the subgroup analysis for focal AIP, the sensitivity for distinguishing between focal AIP and PDAC was lower than that for the overall analysis. MRI had a higher sensitivity than CT (76% vs. 50%, p = 0.28) but a similar specificity (97% vs. 98%, p = 0.07).

CONCLUSION

MRI might be clinically more useful to evaluate patients with AIP, particularly for differentiating AIP from PDAC.

KEY POINTS

• MRI had an overall good diagnostic performance to differentiate AIP from PDAC with a meta-analytic summary estimate of 83% for sensitivity and of 97% for specificity. • CT had a very high specificity (99%), but a suboptimal sensitivity (59%) for differentiating AIP from PDAC. • Compared with CT, MRI had a higher sensitivity, but a similar specificity.

Distinguishing pancreatic cancer and autoimmune pancreatitis with in vivo tomoelastography

OBJECTIVES

To prospectively investigate the stiffness and fluidity of pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP) with tomoelastography, and to evaluate its diagnostic performance in distinguishing the two entities.

METHODS

Tomoelastography provided high-resolution maps of shear wave speed (c in m/s) and phase angle (φ in rad), allowing mechanical characterization of the stiffness and fluidity properties of the pancreas. Forty patients with untreated PDAC and 33 patients with untreated AIP who underwent diagnostic pancreatic MRI at 3-T together with multifrequency MR elastography and tomoelastography data processing were prospectively enrolled. Ten healthy volunteers served as controls. Two radiologists and a technician measured pancreatic stiffness and fluidity independently. The two radiologists also independently evaluated the patients' conventional MR sequences using the following diagnostic score: 1, definitely PDAC; 2, probably PDAC; 3, indeterminate; 4, probably AIP; and 5, definitely AIP. Interobserver agreement was assessed. Stiffness and fluidity of PDAC, AIP, and healthy pancreas, as well as diagnostic performance of tomoelastography and conventional MRI, were compared.

RESULTS

AIP showed significantly lower stiffness and fluidity than PDAC and significantly higher stiffness and fluidity than healthy pancreas. Pancreatic fluidity was not influenced by secondary obstructive changes. The intraclass correlation coefficient for pancreatic stiffness and fluidity by the 3 readers was near-perfect (0.951-0.979, all p < 0.001). Both stiffness and fluidity allowed distinguishing PDAC from AIP. AUCs were 0.906 for stiffness, 0.872 for fluidity, and 0.842 for conventional MRI.

CONCLUSIONS

Pancreatic stiffness and fluidity both allow differentiation of PDAC and AIP with high accuracy.

KEY POINTS

• AIP showed significantly lower stiffness and fluidity than PDAC and significantly higher stiffness and fluidity than healthy pancreas. • Both stiffness and fluidity allowed distinguishing PDAC from AIP. • Pancreatic fluidity could distinguish malignancy from non-malignant secondary obstructive changes.

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

Various inflammatory abnormalities of the pancreas can mimic pancreatic ductal adenocarcinoma (PDAC) at cross-sectional imaging. Misdiagnosis of PDAC at imaging may lead to unnecessary surgery. On the other hand, chronic pancreatitis (CP) bears a greater risk of developing PDAC during the course of the disease. Thus, differentiation between mass-forming chronic pancreatitis (MFCP) and PDAC is important to avoid unnecessary surgery and not to delay surgery of synchronous PDAC in CP.

Imaging features such as the morphology of the mass including displacement of calcifications, presence of duct penetrating, sign appearance of duct stricturing, presence or absence of vessel encasement, apparent diffusion coefficient (ADC) value and intravoxel incoherent motion (IVIM) at diffusion-weighted imaging (DWI), fluorodeoxyglucose (FDG) uptake in PET/CT, and mass perfusion parameters can help to differentiate between PDAC and MFCP. Correct interpretation of imaging features can appropriately guide biopsy and surgery, if necessary. This review summarizes the relevant computed tomography (CT) and magnetic resonance imaging (MRI) features that can help the radiologist to come to a confident diagnosis and to guide further management in equivocal cases.

Pleural, pancreatic and prostatic involvement in IgG4-related disease mimicking pancreatic head malignancy

We describe the case of a gentleman with pleural thickening. On follow-up imaging, dilatation of the main pancreatic and common biliary ducts was noted and an initial diagnosis of pancreatic malignancy was made. During his preoperative workup for pancreatic head malignancy, a PET-CT was performed, which demonstrated increased uptake in the pancreas, in the pleura and in the prostate gland. This raised the possibility of immunoglobulin G4-related disease (IgG4-RD), which was effectively treated with oral steroids. IgG4-RD is a well-described cause of autoimmune pancreatitis but can affect other regions, including the pleura and prostate. It is essential that radiologists are aware of the imaging findings in IgG4-RD and can direct clinicians towards this important multisystem diagnosis.

Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma

PURPOSE

To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann-Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded.

RESULTS

We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann-Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model.

CONCLUSIONS

Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.

Inflammatory mimickers of pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma can be a difficult imaging diagnosis early in its course given its subtle imaging findings such as focal pancreatic duct dilatation, abrupt duct cut-off, and encasement of vasculature. A variety of pancreatitidies have imaging findings that mimic pancreatic ductal adenocarcinoma and lead to mass formation making diagnosis even more difficult on imaging alone. These conditions include acute focal pancreatitis, chronic pancreatitis, autoimmune pancreatitis, and paraduodenal ("groove") pancreatitis. This review will focus on imaging findings that can help differentiate these inflammatory processes from pancreatic ductal adenocarcinoma.

Differentiation of focal autoimmune pancreatitis from pancreatic ductal adenocarcinoma

Autoimmune pancreatitis (AIP) is an inflammatory process of the pancreas that occurs most commonly in elderly males and clinically can mimic pancreatic adenocarcinoma and present with jaundice, weight loss, and abdominal pain. Mass-forming lesions in the pancreas are seen in the focal form of AIP and both clinical and imaging findings can overlap those of pancreatic cancer. The accurate distinction of AIP from pancreatic cancer is of utmost importance as it means avoiding unnecessary surgery in AIP cases or inaccurate steroid treatment in patients with pancreatic cancer. Imaging concomitantly with serological examinations (IgG4 and Ca 19-9) plays an important role in the distinction between these entities. Characteristic extra-pancreatic manifestations as well as favorable good response to treatment with steroids are characteristic of AIP. This paper will review current diagnostic parameters useful in differentiating between focal AIP and pancreatic adenocarcinoma.

Imaging diagnosis and staging of pancreatic ductal adenocarcinoma: a comprehensive review

Pancreatic ductal adenocarcinoma (PDAC) has continued to have a poor prognosis for the last few decades in spite of recent advances in different imaging modalities mainly due to difficulty in early diagnosis and aggressive biological behavior. Early PDAC can be missed on CT due to similar attenuation relative to the normal pancreas, small size, or hidden location in the uncinate process. Tumor resectability and its contingency on the vascular invasion most commonly assessed with multi-phasic thin-slice CT is a continuously changing concept, particularly in the era of frequent neoadjuvant therapy. Coexistent celiac artery stenosis may affect the surgical plan in patients undergoing pancreaticoduodenectomy. In this review, we discuss the challenges related to the imaging of PDAC. These include radiological and clinical subtleties of the tumor, evolving imaging criteria for tumor resectability, preoperative diagnosis of accompanying celiac artery stenosis, and post-neoadjuvant therapy imaging. For each category, the key imaging features and potential pitfalls on cross-sectional imaging will be discussed. Also, we will describe the imaging discriminators of potential mimickers of PDAC.

Diagnostic imaging guide for autoimmune pancreatitis

The International Consensus Diagnosis Criteria for autoimmune pancreatitis (AIP) has been published internationally for the diagnosis of AIP. However, since the revisions in 2006 and 2011, the Clinical Diagnostic Criteria for Autoimmune Pancreatitis 2018 have been published. The criteria were revised based the Clinical Diagnostic Criteria 2011, and included descriptions of characteristic imaging findings such as (1) pancreatic enlargement and (2) distinctive narrowing of the main pancreatic duct. In addition, pancreatic duct images obtained by magnetic resonance cholangiopancreatography as well as conventional endoscopic retrograde pancreatography were newly adopted. The guideline explains some characteristic imaging findings, but does not contain descriptions of the imaging methods, such as detailed imaging parameters and optimal timings of dynamic contrast-enhanced computed tomography/magnetic resonance imaging. It is a matter of concern that imaging methods can vary from hospital to hospital. Although other characteristic findings have been reported, these findings were not described in the guideline. The present paper describes the imaging methods for obtaining optimal images and the characteristic imaging findings with the aim of standardizing image quality and improving diagnostic accuracy when radiologists diagnose AIP in actual clinical settings.

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PURPOSE

The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.

RESULTS

The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0).

CONCLUSIONS

Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.

Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images

Purpose: To investigate the potential of computed tomography (CT) imaging features and texture analysis to differentiate between mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC).

Materials and Methods: Thirty patients with pathologically proved MFP and 79 patients with PDAC were included in this study. Clinical data and CT imaging features of the two lesions were evaluated. Texture features were extracted from arterial and portal phase CT images using commercially available software (AnalysisKit). Multivariate logistic regression analyses were used to identify relevant CT imaging and texture parameters to discriminate MFP from PDAC. Receiver operating characteristic curves were performed to determine the diagnostic performance of predictions.

Results: MFP showed a larger size compared to PDAC (p = 0.009). Cystic degeneration, pancreatic ductal dilatation, vascular invasion, and pancreatic sinistral portal hypertension were more frequent and duct penetrating sign was less frequent in PDAC compared to MFP. Arterial CT attenuation, arterial, and portal enhancement ratios of MFP were higher than PDAC (p < 0.05). In multivariate analysis, arterial CT attenuation and pancreatic duct penetrating sign were independent predictors. Texture features in arterial phase including SurfaceArea, Percentile40, InverseDifferenceMoment_angle90_offset4, LongRunEmphasis_angle45_offset4, and uniformity were independent predictors. Texture features in portal phase including LongRunEmphasis_angle135_offset7, VoxelValueSum, LongRunEmphasis_angle135_offset4, and GLCMEntropy_angle45_offset1 were independent predictors. Areas under the curve of imaging feature-based, texture feature-based in arterial and portal phases, and the combined models were 0.84, 0.96, 0.93, and 0.98, respectively.

Conclusions: CT texture analysis demonstrates great potential to differentiate MFP from PDAC.

A feasible CT feature to differentiate focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma

BACKGROUND

To investigate whether the relative computed tomography (CT) value (rCT) of adjacent pancreatic parenchyma can distinguish focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 13 patients with fAIP and 20 patients with PDAC were included in this study. The rCT was calculated as the ratio of the CT value of adjacent pancreatic parenchyma to that of muscle. The diagnostic performance of rCT for discriminating fAIP from PDAC was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

Both fAIP and PDAC presented hyper-fibrosis histologically and delayed enhancement on CT examination. Moreover, the pancreatic parenchyma of fAIP presented serious inflammation. The mean rCT of the parenchyma was significantly lower in fAIP than in PDAC in all phases. The best diagnostic performance of the rCT value was found in the pancreatic phase, with an area under the ROC curve of 0.912, while the areas under the ROC curve of the portal and delayed phases were 0.812 and 0.754, respectively. The optimal cut-off value for distinguishing fAIP from PDAC was 1.62 in the pancreatic phase.

CONCLUSIONS

The rCT of the pancreatic parenchyma during the pancreatic phase may be a feasible CT feature for differentiating fAIP from PDAC.