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

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.

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Background

Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool.

Results

Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size.

Conclusions

More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01279-4.

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.

Longitudinal evaluation of apparent diffusion coefficient values as a predictor of Prostate Cancer Research International Active Surveillance reclassification

PURPOSE

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer.

METHODS

We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3 years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000 s/mm². For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups.

RESULTS

The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group.

CONCLUSION

The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.

Abnormal Findings on "T1WI or DWI or MRCP:" An Effective Boolean Interpretation Model in Discriminating Small Pancreatic Ductal Adenocarcinoma from Control Group

Objectives

The objectives of the study was to evaluate the diagnostic performance of findings on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and magnetic resonance cholangiopancreatography (MRCP) separately and to identify an optimal Boolean interpretation model for discriminating patients with small pancreatic ductal adenocarcinoma (PDAC) from control groups in clinical practice.

Material and Methods

We retrospectively enrolled 30 patients with surgery confirmed small PDAC (≤20 mm) and 302 patients without pancreatic abnormality between April 2008 and February 2020. The presence of masses was evaluated by T1WI, T2WI, and DWI. Abnormality of the main pancreatic duct (MPD) was evaluated by T2WI and MRCP. Multivariate logistic regression analysis was performed to select significant sequences for discriminating the small PDAC and control groups. Boolean operators "OR" or "AND" were used to construct sequence combinations. Diagnostic performances of these sequences and combinations were evaluated by X ² tests.

Results

The sensitivity of T2WI was lowest (20%) for detecting masses. For evaluating MPD abnormality, sensitivity was higher for MRCP than for T2WI (86.7% vs. 53.3%). Multivariate logistic regression analysis showed that T1WI and DWI for detecting the presence of masses and MRCP for evaluating MPD abnormality were significantly associated with differentiation between the two groups (P = 0.0002, P = 0.0484, and P < 0.0001, respectively). Seven combinations were constructed with T1WI, DWI, and MRCP. The combination of findings on "T1WI or DWI or MRCP" achieved the highest sensitivity of 96.7% and negative predictive value of 99.6%.

Conclusion

The combination of findings on "T1WI or DWI or MRCP" might be an optimal interpretation model for discriminating small PDAC from control groups in clinical practice.

Mathematical model for histogram analysis of dynamic contrast-enhanced MRI: A method to evaluate the drug treatment response in rheumatoid arthritis

PURPOSE

To evaluate the effectiveness of a mathematical model for histogram analysis of DCE-MRI in distinguishing responders from non-responders during RA drug treatment.

METHOD

Twenty-three consecutive RA patients with clinically active inflammation prospectively underwent DCE-MRI at baseline and after treatment. Manual segmentation of the enhanced synovium was performed on the last phase of DCE-MRI. The voxel-based contrast enhancement was calculated in each phase to obtain 75th percentile values. Kinetic curves made from the 75th percentile values were fitted to mathematical model as follows, ΔS(t) = A(1 - e^-αt)e^-βt, where A is the upper limit of signal intensity (%), α (sec^-1) is the rate of signal increase, and β (sec^-1) is the rate of signal decrease during washout. AUC30 was calculated by integration of 30 s. SER was calculated as the signal intensity at the initial time point (t = 60) relative to the delayed time point (t = 300). The volumes of enhanced synovium (sum of the number of voxels) were also calculated.

RESULTS

After treatment, α, Aα, AUC30 and SER were significantly lower in the responder group than in the non-responder group (p = 0.033, 0.024, 0.015, and 0.007). The p value of SER was lowest. Aα, AUC30, and the volume of enhanced synovium had significantly larger changes from baseline to after treatment in the responder group than in the non-responder group (p = 0.045, 0.017, and 0.008). The volume of enhanced synovium had the lowest p value.

CONCLUSIONS

SER after treatment and change in the volume of enhanced synovium might be effective for distinguishing responders from non-responders.

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.