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

CT surveillance for type 1 autoimmune pancreatitis: cumulative radiation dose and diagnostic performance for disease relapse

OBJECTIVES

Long-term follow-up is essential for type 1 autoimmune pancreatitis (AIP) patients due to high relapse rates. The cumulative radiation dose from repeated CT scans during follow-up should not be ignored. We aim to investigate the cumulative radiation dose in AIP patients undergoing CT surveillance and the diagnostic performance of CT in detecting disease relapse. The diagnostic performance of MRI from a secondary cohort during the same period was also investigated.

METHODS

This retrospective single-institutional study included 247 type 1 AIP patients with one or more follow-up CT scans, and 120 patients with MR follow-ups. Four metrics were utilized to report the radiation dose, including the volume computed tomography dose index, the dose length product, size-specific dose estimate and effective dose. The diagnostic performance for AIP relapse was assessed, taking the final clinical diagnosis in retrospect as the reference standard.

RESULTS

With a median 2.3-year follow-up period, AIP patients followed up with CT exhibited a median cumulative radiation dose of 37.5 mSv. 11.3% of patients have accumulated doses exceeding 100 mSv. For the 169 patients followed over a year, 30.8% sustained an average annual radiation dose surpassing 20 mSv. The sensitivity/specificity/accuracy of CT for detecting abdominal organ relapse was 64.1%/99.6%/97.0%. For AIP patients followed up with MRI, the sensitivity for detecting disease relapse was 90.5%.

CONCLUSION

Considering the accumulation of radiation dose in AIP patients and the insufficient sensitivity in detecting disease relapse with CT, safer and more sensitive imaging follow-up strategies should be explored.

KEY POINTS

Question CT, as the primary imaging modality for autoimmune pancreatitis (AIP) follow-up, raises concerns regarding radiation exposure and lacks reported diagnostic performance in detecting AIP relapse. Findings CT in AIP follow-up causes significant cumulative radiation exposure and exhibits insufficient sensitivity in relapse detection. Clinical relevance Type 1 AIP necessitates long-term imaging follow-up, yet current guidelines lack consensus regarding the prioritization of CT or MRI for such follow-up. CT is widely used but has radiation concerns and limited sensitivity, calling for safer, efficient strategies.

Beyond the Common: A Pictorial Review of Rare and Atypical Periampullary Lesions

The periampullary region, defined as the area within 2 cm of the major papilla of the duodenum, is associated with various benign and malignant conditions. Although periampullary cancers are rare, they are the third leading cause of cancer-related deaths in the Western world. Tumors in this area often mimic benign lesions due to overlapping clinical presentations. Diagnostic imaging techniques, including computed tomography, magnetic resonance imaging, ultrasonography, and MR cholangiopancreatography, are commonly used in clinical practice and typically confirmed by pathological analysis. Therefore, evaluating imaging characteristics of rare and atypical periampullary conditions and classifying them into 4 categories (anatomical variations, tumors, inflammatory conditions, and miscellaneous disorders) can aid in the differential diagnosis and improve understanding of these lesions.

Deep learning-based MVIT-MLKA model for accurate classification of pancreatic lesions: a multicenter retrospective cohort study

BACKGROUND

Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions.

METHODS

This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort. A novel hybrid model, Multi-Scale Large Kernel Attention with Mobile Vision Transformer (MVIT-MLKA), was developed, integrating CNN and Transformer architectures to classify pancreatic lesions. The model's performance was compared with traditional machine learning methods and advanced deep learning models. We also evaluated the diagnostic accuracy of radiologists with and without the assistance of the optimal model. Model performance was assessed through discrimination, calibration, and clinical applicability.

RESULTS

The MVIT-MLKA model demonstrated superior performance in classifying pancreatic lesions, achieving an AUC of 0.974 (95% CI 0.967-0.980) in the training set, 0.935 (95% CI 0.915-0.954) in the internal testing set, and 0.924 (95% CI 0.902-0.945) in the external validation set, outperforming traditional models and other deep learning models (P < 0.05). Radiologists aided by the MVIT-MLKA model showed significant improvements in diagnostic accuracy and sensitivity compared to those without model assistance (P < 0.05). Grad-CAM visualization enhanced model interpretability by effectively highlighting key lesion areas.

CONCLUSION

The MVIT-MLKA model efficiently differentiates between benign and malignant pancreatic lesions, surpassing traditional methods and significantly improving radiologists' diagnostic performance. The integration of this advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies.

Hidden in plain sight: commonly missed early signs of pancreatic cancer on CT

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis mostly due to the advanced stage at which disease is diagnosed. Early detection of disease at a resectable stage is, therefore, critical for improving outcomes of patients. Prior studies have demonstrated that pancreatic abnormalities may be detected on CT in up to 38% of CT studies 5 years before clinical diagnosis of PDAC. In this review, we highlight commonly missed signs of early PDAC on CT. Broadly, these commonly missed signs consist of small isoattenuating PDAC without contour deformity, isolated pancreatic duct dilatation and cutoff, focal pancreatic enhancement and focal parenchymal atrophy, pancreatitis with underlying PDAC, and vascular encasement. Through providing commentary on demonstrative examples of these signs, we demonstrate how to reduce the risk of missing or misinterpreting radiological features of early PDAC.

Clinical characteristics, imaging diagnostic accuracy, and prognosis of autoimmune pancreatitis: A real-world study in China

OBJECTIVE

In this study we aimed to comprehensively evaluate the clinical features and treatment outcomes of Chinese patients with autoimmune pancreatitis (AIP) through a single-center real-world study.

METHODS

Patients diagnosed with AIP in Changhai Hospital, Naval Medical University from January 2014 to December 2021 were included. Baseline characteristics, laboratory test results, cross-sectional imaging and endoscopic ultrasound (EUS) findings, and long-term follow-up data were obtained. The differences in these characteristics between type 1 and type 2 AIP patients were analyzed.

RESULTS

Among all 320 patients, 271 (84.7%) and 49 (15.3%) had type 1 and type 2 AIP, respectively. The most common initial symptom was abdominal discomfort (58.1%), followed by obstructive jaundice (32.5%). Extrapancreatic organ involvement was identified in 126 (39.4%) patients, with the biliary system being the most commonly involved (36.6%). Elevated serum IgG4 level was rare in type 2 AIP patients. The diagnostic accuracy of computed tomography (CT), magnetic resonance imaging (MRI), and EUS for definitive and probable AIP were 78.0%, 68.7%, and 80.5%, respectively. EUS-guided tissue acquisition with immunohistochemical staining helped establish a final diagnosis in 39.7% of patients. During the follow-up period of 60 months, 18.6% of patients experienced relapse. The 1-, 3-, and 5-year relapse rates were higher in type 1 AIP patients, with an accumulated rate of 8.0%, 12.6%, and 15.1%, when compared with those with type 2 AIP.

CONCLUSIONS

Type 2 AIP is not uncommon in Chinese population. The diagnostic accuracy of CT and EUS for AIP might be superior to that of MRI.

Radiology domain in the diagnosis of IgG4-RD according to the 2019 American College of Rheumatology and European League Against Rheumatism classification

OBJECTIVES

To evaluate the performance of radiology-related inclusion criteria of the 2019 ACR-EULAR classification system in the diagnosis of IgG4-related disease (IgG4-RD).

METHODS

This retrospective single-institution study included patients who received a diagnosis of IgG4-RD between January 2010 and December 2020. Two abdominal radiologists independently reviewed baseline imaging studies and scored radiology findings according to the 2019 ACR-EULAR classification criteria. Additional scores were assigned based on serological, histopathological, and immunostaining features.

RESULTS

Seventy-four patients (58 males and 16 females) with a mean age of 59.3 ± 13.9 years diagnosed with IgG4-RD were included. 51/74 (68.9%) were classified as having IgG4-RD according to the 2019 ACR-EULAR classification criteria. To reach a score ≥ 20 in these 51 patients, the radiology domain was sufficient in 20/51 (39.2%) and adding the serology domain was required for another 20/51 (39.2%). The remaining 11/51 patients (21.6%) required the histopathology and immunostaining domains. Radiological involvement of two or more organs at presentation was significantly associated with a score of ≥ 20 and seen in 43/51 (84.3%) compared to 5/23 (21.7%) of the non-classified group (p < 0.001). The group classified as having IgG4-RD showed a significantly higher proportion of elevated IgG4 levels (39/51, 76.5%) than the non-classified group (8/23, 34.8%) (< 0.001).

CONCLUSION

The study findings support the effectiveness of the radiology-related inclusion criteria of the 2019 ACR-EULAR classification system in diagnosing IgG4-RD. Combining radiology and serology domains achieved the cut-off in 80% of IgG-RD patients, enabling non-invasive diagnosis. The classification of IgG4-RD was significantly associated with multi-organ involvement, particularly affecting the pancreas and biliary system.

CRITICAL RELEVANCE STATEMENT

This study is the first to evaluate the diagnostic performance of the radiology domain in the 2019 ACR-EULAR classification criteria. The study results confirm its utility and potential to enable non-invasive diagnosis when combined with serological testing in a significant proportion of patients.

KEY POINTS

• A significant proportion of patients can be diagnosed with IgG4-RD using the radiology and serology domains exclusively. • Multi-organ involvement is significantly associated with classifying patients as IgG4-RD, with the pancreas and biliary system most frequently affected. • A high level of inter-reader agreement in the scoring of the radiology domain supports its reliability.

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.

18F-FAPI-04 Outperforms 18F-FDG PET/CT in Clinical Assessments of Patients with Pancreatic Adenocarcinoma

Accurate diagnosis and staging are crucial for selecting treatment for patients with pancreatic ductal adenocarcinoma (PDAC). The desmoplastic responses associated with PDAC are often characterized by hypometabolism. Here, we investigated 18F-fibroblast activation protein inhibitor (FAPI)-04 PET/CT in evaluation of PDAC and compared the findings with those obtained using 18F-FDG. Methods: Sixty-two PDAC patients underwent 18F-FAPI-04 PET/CT and 18F-FDG PET/CT. Identification of primary lesions, lymph node (LN) metastasis, and distant metastasis (DM) by these methods was evaluated, and TNM staging was performed. Correlation between SUVmax of the primary lesion and treatment response was explored in patients who received systemic therapy. Results: 18F-FAPI-04 PET/CT identified all patients with PDAC; 18F-FDG PET/CT missed 1 patient. Tracer uptake was higher in 18F-FAPI-04 PET/CT than in 18F-FDG PET/CT in primary tumors (10.63 vs. 2.87, P < 0.0001), LN metastasis (2.90 vs. 1.43, P < 0.0001), and DM (liver, 6.11 vs. 3.10, P = 0.002; peritoneal, 4.70 vs. 2.08, P = 0.015). The methods showed no significant difference in the T staging category, but the N and M values were significantly higher for 18F-FAPI-04 PET/CT than for 18F-FDG PET/CT (P = 0.002 and 0.008, respectively). Thus, 14 patients were upgraded, and only 1 patient was downgraded, by 18F-FAPI-04 PET/CT compared with 18F-FDG PET/CT. A high SUVmax of the primary tumor did not correlate with treatment response for either 18F-FAPI-04 or 18F-FDG. Conclusion: 18F-FAPI-04 PET/CT performed better than 18F-FDG PET/CT in identification of primary tumors, LN metastasis, and DM and in TNM staging of PDAC.

Potential applications of PET/MRI in non-oncologic conditions within the abdomen and pelvis

PET/MRI is a relatively new imaging modality with several advantages over PET/CT that promise to improve imaging of the abdomen and pelvis for specific diagnostic tasks by combining the superior soft tissue characterization of MRI with the functional information acquired from PET. PET/MRI has an established role in staging and response assessment of multiple abdominopelvic malignancies, but the modality is not yet established for non-oncologic conditions of the abdomen and pelvis. In this review, potential applications of PET/MRI for non-oncologic conditions of abdomen and pelvis are outlined, and the available literature is reviewed to highlight promising areas for further research and translation into clinical practice.

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.

Autoimmune pancreatitis type 2 (idiopathic duct-centric pancreatitis): A comprehensive review

Autoimmune pancreatitis (AIP) is an uncommon fibro-inflammatory disorder precipitated by autoimmune/inflammatory reactions. Currently, there are two clinical subtypes of AIP (type 1 [AIP-1] and type 2 [AIP-2]) that correspond to two histologic descriptors (lymphoplasmacytic sclerosing pancreatitis and idiopathic duct-centric pancreatitis, respectively). While our understanding of AIP-1 has evolved considerably over the years, little is known about AIP-2 due to its rarity, often leading to misdiagnosis, delayed treatment, and even unnecessary surgical resection. Compared to AIP-1, AIP-2 exhibits distinct clinical and histologic features. Because AIP-2 is a pancreas-restricted disease without a specific serum marker, the evaluation of histologic features (e.g., granulocytic epithelial lesions) is essential for an accurate diagnosis. Patients with AIP-2 respond well to glucocorticoids, with anti-tumor necrosis factor-alpha antibodies as a promising alternative therapy. The prognosis of AIP-2 is generally favorable and relapse is uncommon. Here, we provide an overview of our current knowledge on the clinical features, diagnosis, therapeutic regimens, prognosis, and putative mechanisms underlying AIP-2. Notably, the diagnostic differentiation between AIP-2, especially the mass-forming/focal type, and pancreatic cancer is important, but challenging. In this regard, endoscopic ultrasound-guided core biopsy has a key role, but novel diagnostic markers and modalities are clearly needed.

Establishment and Validation of a Nomogram Prediction Model for the Severe Acute Pancreatitis

Background

Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction, and blood clotting within 48 hours of its onset, and is associated with a high mortality rate. The aim of this study was to establish a reliable diagnostic prediction model for the early stage of severe pancreatitis.

Methods

The clinical data of patients diagnosed with acute pancreatitis from October 2017 to June 2022 at the Shangluo Central Hospital were collected. The risk factors were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. A novel nomogram model was then established by multivariable logistic regression analysis.

Results

The data of 436 patients with acute pancreatitis, 45 (10.3%) patients had progressed to SAP. Through univariate and LASSO regression analyses, the neutrophils (P <0.001), albumin (P < 0.001), blood glucose (P < 0.001), serum calcium (P < 0.001), serum creatinine (P < 0.001), blood urea nitrogen (P < 0.001) and procalcitonin (P = 0.005) were identified as independent predictive factors for SAP. The nomogram built on the basis of these factors predicted SAP with sensitivity of 0.733, specificity of 0.9, positive predictive value of 0.458 and negative predictive value of 0.967. Furthermore, the concordance index of the nomogram reached 0.889 (95% CI, 0.837-0.941), and the area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis was significantly higher than that of the APACHEII and ABISAP scoring systems. The established model was validated by plotting the clinical decision curve analysis (DCA) and clinical impact curve (CIC).

Conclusion

We established a nomogram to predict the progression of early acute pancreatitis to SAP with high discrimination and accuracy.

Serum biomarkers for the differentiation of autoimmune pancreatitis from pancreatic ductal adenocarcinoma

Autoimmune pancreatitis (AIP), a chronic inflammation caused by the immune system attacking the pancreas, usually presents imaging and clinical features that overlap with those of pancreatic ductal adenocarcinoma (PDAC). Serum biomarkers, substances that quantitatively change in sera during disease development, are a promising non-invasive tool with high utility for differentiating between these diseases. In this way, the presence of AIP is currently suspected when serum concentrations of immunoglobulin G4 (IgG4) antibody are elevated. However, this approach has some drawbacks. Notably, IgG4 antibody concentrations are also elevated in sera from some patients with PDAC. This review focuses on the most recent and relevant serum biomarkers proposed to differentiate between AIP and PDAC, evaluating the usefulness of immunoglobulins, autoantibodies, chemokines, and cytokines. The proposed serum biomarkers have proven useful, although most studies had a small sample size, did not examine their presence in patients with PDAC, or did not test them in humans. In addition, current evidence suggests that a single serum biomarker is unlikely to accurately differentiate these diseases and that a set of biomarkers will be needed to achieve adequate specificity and sensitivity, either alone or in combination with clinical data and/or radiological images.

Measuring quality of reporting in systematic reviews of diagnostic test accuracy studies in medical imaging: comparison of PRISMA-DTA and PRISMA

OBJECTIVES

To compare the reporting quality measured by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy studies (PRISMA-DTA) vs the original PRISMA checklist for systematic reviews of diagnostic test accuracy studies in imaging and survey the use of PRISMA-DTA by researchers and endorsement by journals.

METHODS

Systematic reviews of DTA studies published in 2020 and 2021 in Quartile 1 and Quartile 3 medical imaging journals (defined by Journal Citation Reports) were identified through PubMed. The reporting of each systematic review was assessed using PRISMA-DTA, PRISMA-2009 and PRISMA-2020. The item scores and overall score were compared among the three checklists. We also examined checklist adoption by the included systematic reviews and surveyed checklist endorsement from author instructions of included journals.

RESULTS

A total of 173 systematic reviews from 66 journals were included. The use of PRISMA-DTA, compared with PRISMA-2009 and PRISMA-2020, identified more issues in the reporting of title (proportion of systematic reviews with proper reporting, 27.2% vs 98.8% vs 98.8%), abstract (39.3% vs 97.1% vs 64.7%), eligibility criteria (67.6% vs 94.2% vs 94.2%), search (28.9% vs 72.3% vs 28.9%), definitions for data extraction (14.5% vs 91.9% vs 91.9%), diagnostic accuracy measures (38.2% vs 93.6% vs 93.6%), synthesis of results (28.9% vs 89.6% vs 73.4%) and results of individual studies (40.5% vs 80.3% vs 80.3%). The overall median reporting score measured by PRISMA-DTA (72.0% (interquartile range (IQR), 66.7-77.8%)) was lower than that measured by PRISMA-2009 (88.9% (IQR, 84.0-92.6%)) and similar to that measured by PRISMA-2020 (74.1% (IQR, 66.7-77.8%)). Additionally, PRISMA-DTA was used by only 43 (24.9%) systematic reviews and endorsed by two (3.0%) journals. These trends remained consistent for reviews published in journals with diverse scientific impact.

CONCLUSIONS

The use of PRISMA-DTA may identify more reporting inadequacies compared with the original PRISMA checklists when assessing diagnostic test accuracy systematic reviews, especially in critical sections such as title, abstract and methods. However, this tool is not commonly used by researchers and is inadequately endorsed by imaging journals. Our findings suggest a strong need to use PRISMA-DTA for reporting of diagnostic test accuracy systematic reviews by authors and its endorsement by journals. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images

PURPOSE

To explore a multidomain fusion model of radiomics and deep learning features based on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis.

MATERIALS AND METHODS

This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on ¹⁸F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models.

RESULTS

The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model.

CONCLUSION

The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on ¹⁸F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

The diagnostic performance of contrast-enhanced ultrasound versus contrast-enhanced computed tomography for pancreatic carcinoma: a systematic review and meta-analysis

BACKGROUND

Pancreatic carcinoma is a highly fatal disease, and early diagnosis is of vital importance. This meta-analysis aimed to compare the diagnostic performances of contrast-enhanced ultrasonography (CEUS) against contrast-enhanced computed tomography (CECT) for pancreatic carcinoma, using pathological results or alternative imaging modality as the gold standard.

METHODS

A thorough search was conducted in PubMed, EMBASE, Web of Science and Cochrane Library databases. Two investigators selected the studies and extracted the data independently. A bivariate mixed-effects regression model was used to calculate the pooled data. Subgroup analysis and meta-regression were performed to explore the causes of heterogeneity.

RESULTS

A total of 1,227 records were identified, of which 7 articles with 588 patients were assessed for eligibility. The overall sensitivity, specificity of CEUS and CECT with their 95% confidential intervals (95% CI) were 0.91 (0.85-0.94) and 0.88 (0.81-0.92), 0.83 (0.70-0.91) and 0.87 (0.73-0.94), respectively. The area under curve (AUC) of CEUS and CECT were 0.94 and 0.93. Subgroup analysis showed CEUS may be good at diagnosing lesions with diameters less than 2 cm. Tumor features, region and study type were the main causes of heterogeneity.

CONCLUSIONS

CEUS has a satisfying diagnostic performance for pancreatic carcinoma and it has high sensitivity for small pancreatic carcinomas (≤2 cm); besides, it performs well in discriminating pancreatic cancer from chronic pancreatitis. Therefore, CEUS can be a useful supplement to CECT.

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).

Autoimmune Pancreatitis and Immunoglobulin G4-related Sclerosing Cholangitis: Past, Present, and Future

The emergence of glucocorticoid-responsive autoimmune pancreatitis (AIP) and IgG4-related sclerosing cholangitis (IgG4-SC), a new disease entity, has attracted considerable interest within the international gastroenterology community. The typical manifestations of AIP/IgG4-SC are obstructive jaundice and pancreatic enlargement in the elderly, which may mimic the presentations of pancreatobiliary malignancies. The timely diagnosis of AIP/IgG4-SC can lead to adequate glucocorticoid treatment, whereas a misdiagnosis can result in unnecessary major surgery. The diagnostic criteria used to diagnose AIP include several cardinal features of AIP that can be detected via pancreatic parenchymal imaging, ductal imaging, serum IgG4 levels, histopathology, other organ involvement, and response to glucocorticoid therapy. The differential diagnosis of AIP/IgG4-SC may include pancreatobiliary malignancies and primary sclerosing cholangitis. Although most patients with AIP/IgG4-SC respond well to glucocorticoid therapy, there is a frequent relapse of the disease in the long term. This review describes the evolution of the concept of AIP and IgG4-related disease, including the development of diagnostic criteria, discusses the current practice for diagnosis and treatment, and suggests prospects for research.

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.

Computed tomography perfusion imaging evaluation of angiogenesis in patients with pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma is one of the most common malignant tumors of the digestive system. More than 80% of patients with pancreatic adenocarcinoma are not diagnosed until late stage and have distant or local metastases.

AIM

To investigate the value of computed tomography (CT) perfusion imaging in the evaluation of angiogenesis in pancreatic adenocarcinoma patients.

METHODS

This is a retrospective cohort study. Patients with pancreatic adenocarcinoma and volunteers without pancreatic diseases underwent CT perfusion imaging from December 2014 to August 2017 in Huashan Hospital, Fudan University Shanghai, China.

RESULTS

A total number of 35 pancreatic adenocarcinoma patients and 33 volunteers were enrolled. The relative blood flow (rBF), and relative blood volume (rBV) were significantly lower in patients with pancreatic adenocarcinoma than in the control group (P < 0.05). Conversely, the relative permeability in patients with pancreatic adenocarcinoma was significantly higher than that in controls (P < 0.05). In addition, rBF, rBV, and the vascular maturity index (VMI) were significantly lower in grade III-IV pancreatic adenocarcinoma than in grade I-II pancreatic adenocarcinoma (P < 0.05). Vascular endothelial growth factor (VEGF), CD105-MVD, CD34-MVD, and angiogenesis rate (AR) were significantly higher in grade III-IV pancreatic adenocarcinoma than in grade I-II pancreatic adenocarcinoma (P < 0.05). Significant correlations between rBF and VEGF, CD105-MVD, AR, and VMI (P < 0.01) were observed. Moreover, the levels of rBV were statistically significantly correlated with those of VEGF, CD105-MVD, CD34-MVD, and VMI (P < 0.01).

CONCLUSION

Perfusion CT imaging may be an appropriate approach for quantitative assessment of tumor angiogenesis in pancreatic adenocarcinoma.

Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study

BACKGROUND

Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM.

PURPOSES

To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals.

FIELD STRENGTH AND SEQUENCES

A 3.0 T and fat-suppressed T1-weighted imaging.

ASSESSMENT

Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase.

STATISTICAL TESTS

Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant.

RESULTS

The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness.

DATA CONCLUSION

Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Application of dynamic contrast enhanced ultrasound in distinguishing focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma

OBJECTIVE

To explore the value of dynamic contrast enhanced ultrasound (DCE-US) in preoperative differential diagnosis of focal-type autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC).

PATIENTS AND METHODS

From May 2016 to March 2020, patients with biopsy and histopathologically confirmed focal-type AIP (n = 9) were retrospectively included. All patients received contrast enhanced ultrasound (CEUS) examinations one week before surgery/biopsy. Dynamic analysis was performed by VueBox® software (Bracco, Italy). Eighteen cases of resection and histopathologically proved PDAC lesions were also included as control group. B mode ultrasound (BMUS) features, CEUS enhancement patterns, time intensity curves (TICs) and CEUS quantitative parameters were obtained and compared between AIP and PDAC lesions.

RESULTS

After injection of ultrasound contrast agents, most focal-type AIP lesions displayed hyper-enhancement (2/9, 22.2%) or iso-enhancement (6/9, 66.7%) during arterial phase of CEUS, while most of PDAC lesions showed hypo-enhancement (88.9%) (P < 0.01). During late phase, most of AIP lesions showed iso-enhancement (8/9, 88.9%), while most of PDAC lesions showed hypo-enhancement (94.4%) (P < 0.001). Compared with PDAC lesions, TICs of AIP lesions showed delayed and higher enhancement. Among all CEUS perfusion parameters, ratio of PE (peak enhancement), WiAUC (wash-in area under the curve), WiR (wash-in rate), WiPI (wash-in perfusion index, WiPI = WiAUC/ rise time), WoAUC (wash-out area under the curve), WiWoAUC (wash-in and wash-out area under the curve) and WoR (wash-out rate) between pancreatic lesion and surrounding normal pancreatic tissue were significantly higher in AIP lesions than PDAC lesions (P < 0.05).

CONCLUSION

DCE-US with quantitative analysis has the potential to make preoperative differential diagnosis between focal-type AIP and PDAC non-invasively.