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

Quantitative Evaluation of Acute Pancreatitis Based on Dual-Energy Computed Tomography

OBJECTIVE

To evaluate the value of dual-energy computed tomography (DECT) parameters for the quantitative diagnosis of acute pancreatitis (AP) and classification of its severity.

METHODS

Patients with AP underwent a plain CT scan and three contrast-enhanced DECT scans. We analyzed the group differences in iodine concentration (IC) and slope of the spectral Hounsfield unit curve (λHU) of the 3-phase enhanced scans (arterial, venous, and delayed phases).

RESULTS

The study included 60 AP patients (38 males and 22 females; mean age: 47.43±13.47 y). On the basis of the CT severity index (CTSI), the patients were divided into 2 groups: group A (mild AP, n=26) and group B (moderate/severe AP, n=34). IC and λHU in the arterial and venous phases were all significantly higher in group A than in group B (P<0.001) and could effectively differentiate the 2 groups. The areas under the curve were 0.753 (95% CI: 0.624-0.855), 0.799 (95% CI: 0.676-0.892), 0.774 (95% CI: 0.647-0.872), and 0.842 (95% CI: 0.724-0.923) for IC at arterial and venous phases and λHU at arterial and venous phases, respectively. These parameters decreased with the increase of CTSI, showing significant negative correlations, with r were -0.512 (95% CI: -0.678 to -0.297), -0.492 (95% CI: -0.663 to -0.272), -0.552 (95% CI: -0.707 to -0.346), -0.569 (95% CI: -0.719 to -0.368) for IC at arterial and venous phases and λHU at arterial and venous phases, respectively (P<0.001).

CONCLUSIONS

DECT imaging can quantitatively analyze AP, and the IC and λHU can be used to distinguish mild and severe cases, adding functional information to the CT morphology to determine the severity and prognosis of the disease.

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma

BACKGROUND

This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).

RESULTS

The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit.

CONCLUSIONS

The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Dual-layer spectral detector CT: A noninvasive preoperative tool for predicting histopathological differentiation in pancreatic ductal adenocarcinoma

PURPOSE

To predict histopathological differentiation grades in patients with pancreatic ductal adenocarcinoma (PDAC) before surgery with quantitative and qualitative variables obtained from dual-layer spectral detector CT (DLCT).

METHODS

Totally 128 patients with histopathologically confirmed PDAC and preoperative DLCT were retrospectively enrolled and categorized into the low-grade (LG) (well and moderately differentiated, n = 82) and high-grade (HG) (poorly differentiated, n = 46) subgroups. Both conventional and spectral variables for PDAC were measured. The ratio of iodine concentration (IC) values in arterial phase(AP) and venous phase (VP) was defined as iodine enhancement fraction_AP/VP (IEF_AP/VP). Necrosis was visually assessed on both conventional CT images (necrosis_con) and virtual mono-energetic images (VMIs) at 40 keV (necrosis_40keV). Forward stepwise logistic regression method was conducted to perform univariable and multivariable analysis. Receiver operating characteristic (ROC) curves and the DeLong method were used to evaluate and compare the efficiencies of variables in predicting tumor grade.

RESULTS

Necrosis_con (odds ratio [OR] = 2.84, 95% confidence interval [CI]: 1.13-7.13; p < 0.001) was an independent predictor among conventional variables, and necrosis_40keV (OR = 5.82, 95% CI: 1.98-17.11; p = 0.001) and IEF_AP/VP (OR = 1.12, 95% CI:1.07-1.17; p < 0.001) were independent predictors among spectral variables for distinguishing LG PDAC from HG PDAC. IEF_AP/VP (AUC = 0.754, p = 0.016) and combination model (AUC = 0.812, p < 0.001) had better predictive performances than necrosis_con (AUC = 0.580). The combination model yielded the highest sensitivity (72%) and accuracy (79%), while IEF_AP/VP exhibited the highest specificity (89%).

CONCLUSION

Variables derived from DLCT have the potential to preoperatively evaluate PDAC tumor grade. Furthermore, spectral variables and their combination exhibited superior predictive performances than conventional CT variables.

Combination of Serum CA 19-9 and Endoscopic Ultrasound Findings Can Predict Malignancy Risk in Patients With Chronic Pancreatitis Presenting With Pancreatic Head Mass: A Proof-of-Concept Study

BACKGROUND AND AIMS

Inflammatory head mass of pancreas (IMP) developing in background of chronic pancreatitis (CP) is difficult to distinguish from carcinoma pancreas. We aimed to delineate natural course of IMP and predict their malignancy risk, avoiding unnecessary biopsies.

MATERIALS AND METHODS

In this retrospective single-center study, clinical records of patients with CP with diagnosed pancreatic head mass were reviewed. Clinical, laboratory, imaging, endoscopic findings, and follow-up details were retrieved from prospectively maintained database. A diagnostic nomogram was developed combining serum cancer antigen 19-9 and endoscopic ultrasound (EUS) findings to predict the risk of malignancy.

RESULTS

We identified 107 patients with pancreatic head mass with CP of whom 87 (81.3%) were IMP and 20 (18.7%) were malignant. Patients with IMP were more frequently young males with alcohol-related CP and low CA 19-9 in comparison with those with malignancy (age IMP: 41.3 ± 11.3 vs carcinoma: 49.3 ± 14.5 years [ P = 0.009]; males 89.7% vs 65% [ P = 0.011]; alcoholic etiology: 71.3% vs 20% [ P < 0.001]; median CA 19-9: 25.78 [interquartile range, 7.20-120.60] vs 1034.50 [106.65-7808.25] [ P < 0.001]). A diagnostic nomogram combining CA 19-9 and EUS findings could identify malignancy with an optimism-corrected c-statistic of 0.905, which was better than both CA 19-9 (0.80) and EUS alone (0.826). Patients with IMP had relatively benign disease course with 40.2% biliary obstruction, 20.7% portal venous thrombosis, 14.9% gastric outlet obstruction, and 1-, 3-, and 5-year survival being 97.3%, 92.7%, and 92.0%, respectively. Surgery was required in only 12 patients (13.8%) with IMP.

CONCLUSIONS

Combination of CA 19-9 and EUS best identifies malignancy risk in patients with IMP, who have otherwise benign course.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas

PURPOSE

To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9).

METHODS

This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test.

RESULTS

The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076).

CONCLUSIONS

Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

Progress in the application of dual-energy CT in pancreatic diseases

Pancreatic diseases are difficult to diagnose due to their insidious onset and complex pathophysiological developmental characteristics. In recent years, dual-energy computed tomography (DECT) imaging technology has rapidly advanced. DECT can quantitatively extract and analyze medical imaging features and establish a correlation between these features and clinical results. This feature enables the adoption of more modern and accurate clinical diagnosis and treatment strategies for patients with pancreatic diseases so as to achieve the goal of non-invasive, low-cost, and personalized treatment. The purpose of this review is to elaborate on the application of DECT for the diagnosis, biological characterization, and prediction of the survival of patients with pancreatic diseases (including pancreatitis, pancreatic cancer, pancreatic cystic tumor, pancreatic neuroendocrine tumor, and pancreatic injury) and to summarize its current limitations and future research prospects.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Benefits of Dual-Layer Spectral CT Imaging in Staging and Preoperative Evaluation of Pancreatic Ductal Adenocarcinoma

Imaging of pancreatic malignancies is challenging but has a major impact on the patients therapeutic approach and outcome. In particular with pancreatic ductal adenocarcinoma (PDAC), usually a hypovascularized tumor, conventional CT imaging can be prone to errors in determining tumor extent and presence of metastatic disease. Dual-layer spectral detector CT (SDCT) is an emerging technique for acquiring spectral information without the need for prospective patient selection or specific protocols, with a detector capable of differentiating high- and low-energy photons to acquire full spectral images. In this review, we present the diagnostic benefits and capabilities of modern SDCT imaging with a focus on PDAC. We highlight the most useful virtual reconstructions in oncologic imaging and their benefits in staging and assessment of resectability in PDAC, including the assessment of tumor extent, vascular infiltration, and metastatic disease. We present imaging examples on a latest-generation SDCT scanner.

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.

Comprehensive multimodal management of borderline resectable pancreatic cancer: Current status and progress

Borderline resectable pancreatic cancer (BRPC) is a complex clinical entity with specific biological features. Criteria for resectability need to be assessed in combination with tumor anatomy and oncology. Neoadjuvant therapy (NAT) for BRPC patients is associated with additional survival benefits. Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT. More attention to management standards during NAT, including biliary drainage and nutritional support, is needed. Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period, including NAT responsiveness and the selection of surgical timing.

Spectral computed tomography with inorganic nanomaterials: State-of-the-art

Recently, spectral computed tomography (CT) technology has received great interest in the field of radiology. Spectral CT imaging utilizes the distinct, energy-dependent X-ray absorption properties of substances in order to provide additional imaging information. Dual-energy CT and multi-energy CT (Spectral CT) are capable of constructing monochromatic energy images, material separation images, energy spectrum curves, constructing effective atomic number maps, and more. However, poor contrast, due to neighboring X-ray attenuation of organs and tissues, is still a challenge to spectral CT. Hence, contrast agents (CAs) are applied for better differentiation of a given region of interest (ROI). Currently, many different kinds of inorganic nanoparticulate CAs for spectral CT have been developed due to the limitations of clinical iodine (I)-based contrast media, leading to the conclusion that inorganic nanomedicine applied to spectral CT will be a powerful collaboration both in basic research and in clinics. In this review, the underlying principles and types of spectral CT techniques are discussed, and some evolving clinical diagnosis applications of spectral CT techniques are introduced. In particular, recent developments in inorganic CAs used for spectral CT are summarized. Finally, the challenges and future developments of inorganic nanomedicine in spectral CT are briefly discussed.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Dual-Energy CT in the Pancreas

Dual-energy computed tomography (DECT) is an evolving imaging technology that is gaining popularity, particularly in different abdominopelvic applications. Essentially, DECT uses two energy spectra simultaneously to acquire CT attenuation data which is used to distinguish among structures with different tissue composition. The wide variety of reconstructed image data sets makes DECT especially attractive in pancreatic imaging. This article reviews the current literature on DECT as it applies to imaging the pancreas, focusing on pancreatitis, trauma, pancreatic ductal adenocarcinoma, and other solid and cystic neoplasms. The advantages of DECT over conventional CT are highlighted, including improved lesion detection, radiation dose reduction, and enhanced image contrast. Additionally, data exploring the ideal protocol for pancreatic imaging using DECT is reviewed. Finally, limitations of DECT in pancreatic imaging as well as recommendations for future research are provided.

Value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma and type II papillary renal cell carcinoma

BACKGROUND

Energy spectrum computed tomography (CT) has become a promising approach for the differential diagnosis of tumor subtypes.

PURPOSE

To explore the value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (pRCC).

MATERIAL AND METHODS

Forty-two cases of high-grade ccRCC and 28 cases of type II pRCC were retrospectively reviewed. All region of interest (ROI) measurements were maintained consistently between the two-phase contrast-enhanced examinations. The ROIs encompassed as much of the enhancing areas of the lesions as possible. Energy spectrum CT parameters of all cases, including the 70 keV (HU) value, normalized iodine concentration (NIC), and energy spectrum curve slope were recorded by two radiologists with over 10 years of experience in abdominal CT diagnosis.

RESULTS

In the cortical phase (CP) and parenchymal phase (PP), the 70 keV (HU) value, NIC, and slope value of the energy spectrum curve of high-grade ccRCC were significantly higher than those of type II pRCC. In the CP, NIC showed the highest differential diagnosis efficiency for the two group tumors, with a sensitivity of 78.9% and a specificity of 77.0%. There was no statistical difference in tumor hemorrhage, tumor envelope, tumor morphology, tumor border, lymph node metastasis, embolism, renal pelvis invasion, or tumor calcification between the two tumor types. However, there was significant difference in the number of tumors (P = 0.019).

CONCLUSION

Energy spectrum CT parameters are valuable for the differential diagnosis of high-grade ccRCC and type II pRCC.

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

PURPOSE

It is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions.

METHODS

A total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts.

RESULTS

ADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group.

CONCLUSION

The mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.

Utility of Quantitative Metrics from Dual-Layer Spectral-Detector CT for Differentiation of Pancreatic Neuroendocrine Tumor and Neuroendocrine Carcinoma

Background: The 2019 WHO classification separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NET) and neuroendocrine carcinomas (NEC), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. Objective: To assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. Methods: This retrospective study included 104 patients (mean age 51±13 years; 53 women, 51 men) with pathologically confirmed NEN [89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3 (G3); 15 NEC], who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases, and also assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilation, vascular invasion, lymphadenopathy). Interreader agreement was assessed. Mean values between readers were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. Results: Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.879 to 0.992 for quantitative metrics, and, expressed as kappa coefficients, from 0.763 to 0.823 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (P<.05) were IC in portal venous phase (1.3 mg/mL in NEC vs 2.7 mg/mL in NET), Zeff in portal venous phase (8.1 vs 8.6), and attenuation in portal venous phase (78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on combination of IC and Zeff achieved sensitivity of 93.3% and specificity of 80.9% for NEC. Significant independent predictors (P<.05) for differentiating G3 NET and NEC were IC (1.3 vs 2.0 mg/mL; AUC=0.789) and attenuation (78.2 vs 90.3 HU; AUC=0.647), both measured in portal venous phase. Conclusion: Incorporation of DLCT-metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. Clinical Impact: DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.

Spectral CT of the abdomen: Where are we now?

Spectral CT adds a new dimension to radiological evaluation, beyond assessment of anatomical abnormalities. Spectral data allows for detection of specific materials, improves image quality while at the same time reducing radiation doses and contrast media doses, and decreases the need for follow up evaluation of indeterminate lesions. We review the different acquisition techniques of spectral images, mainly dual-source, rapid kV switching and dual-layer detector, and discuss the main spectral results available. We also discuss the use of spectral imaging in abdominal pathologies, emphasizing the strengths and pitfalls of the technique and its main applications in general and in specific organs.

Simulated twin-phase pancreatic CT generated using single portal venous phase dual-energy CT acquisition in pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the diagnostic performance of a simulated twin-phase pancreatic protocol CT generated from a single portal venous phase (PVP) dual-energy CT (DECT) acquisition in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this retrospective study, we included 63 patients with PDAC who underwent pancreatic protocol (pancreatic phase [PP] and PVP) DECT. Two data sets were created from this original acquisition-(1) Standard protocol (50 keV PP/65 keV PVP) and (2) Simulated protocol (40 keV/65 keV PVP). Using a 5-point scale, three readers scored image quality, tumor conspicuity, and arterial involvement by the PDAC. Signal-to-noise ratio (SNR) of the pancreas and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative scores, quantitative parameters, and radiation dose were compared between standard and simulated protocols.

RESULTS

No significant difference in detection rate of PDAC was seen between the standard (58/63, 92.1%) and simulated protocols (56/63, 88.9%) (P = 0.76). Subjective scoring for arterial involvement for celiac (P = 0.86), superior mesenteric (P = 0.88), splenic (P = 0.86), common hepatic (P = 0.52), gastroduodenal (P = 0.95), first jejunal (P = 0.48) arteries, and aorta (P = 1.00) were comparable between two protocols. The image quality (P = 0.14), the SNR of the pancreas (P = 0.15), and CNR (P = 0.54) were comparable between two protocols. The projected mean dose-length product (DLP) (629.6 ± 148.3 mGy cm) in the simulated protocol showed a 44% reduction in radiation dose compared to the standard protocol (mean DLP, 1123.3 ± 268.9 mGy cm) (P < 0.0001).

CONCLUSIONS

Low keV images generated from a PVP DECT acquisition allows creation of a twin-phase pancreatic protocol CT with comparable diagnostic accuracy for detecting PDAC with significant reduction in radiation dose. Reduced radiation dose is desirable in surveillance and screening for pancreatic diseases.

Material decomposition using iodine quantification on spectral CT for characterising nodules in the cirrhotic liver: a retrospective study

Background

There is limited scientific evidence on the potential of spectral computed tomography (SCT) for differentiation of nodules in the cirrhotic liver. We aimed to assess SCT-generated material density (MD) parameters for nodule characterisation in cirrhosis.

Methods

Dynamic dual-energy SCT scans of cirrhotic patients performed over 3 years were retrospectively reviewed. They were classified as hepatocellular carcinoma (HCC), regenerative or indeterminate, according to the European Association for the Study of the Liver criteria. MD maps were generated to calculate the area under the curve (AUC) and cutoff values to discriminate these nodules in the hepatic arterial phase (HAP) and portal venous phase (PVP). MD maps included iodine concentration density (ICD) of the liver and nodule, lesion-to-normal liver ICD ratio (LNR) and difference in nodule ICD between HAP and PVP.

Results

Three hundred thirty nodules belonging to 300 patients (age 53.0 ± 12.7 years, mean ± standard deviation) were analysed at SCT (size 2.3 ± 0.8 cm, mean ± SD). One hundred thirty-three (40.3%) nodules were classified as HCC, 147 (44.5%) as regenerative and 50 (15.2%) as indeterminate. On histopathology, 136 (41.2%) nodules were classified as HCC, 183 (55.5%) as regenerative and 11 (3.3%) as dysplastic. All MD parameters on HAP and the nodule  difference in ICD could discriminate pathologically proven HCC or potentially malignant nodules from regenerative nodules (p < 0.001). The AUC was 82.4% with a cutoff > 15.5 mg/mL for nodule ICD, 81.3% > 1.8 for LNR-HAP and 81.3% for difference in ICD > 3.5 mg/mL.

Conclusion

SCT-generated MD parameters are viable diagnostic tools for differentiating malignant or potentially malignant from benign nodules in the cirrhotic liver.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-021-00220-6.

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

BACKGROUND

It is difficult to identify pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions through conventional CT or MR examination. As an innovative image analysis method, radiomics may possess potential clinical value in identifying PDAC and MFCP. To develop and validate radiomics models derived from multiparametric MRI to distinguish pancreatic ductal adenocarcinoma (PDAC) and mass-forming chronic pancreatitis (MFCP) lesions.

METHODS

This retrospective study included 119 patients from two independent institutions. Patients from one institution were used as the training cohort (51 patients with PDAC and 13 patients with MFCP), and patients from the other institution were used as the testing cohort (45 patients with PDAC and 10 patients with MFCP). All the patients had pathologically confirmed results, and preoperative MRI was performed. Four feature sets were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and the artery (A) and portal (P) phases of dynamic contrast-enhanced MRI, and the corresponding radiomics models were established. Several clinical characteristics were used to discriminate PDAC and MFCP lesions, and clinical model was established. The results of radiologists' evaluation were compared with pathology and radiomics models. Univariate analysis and the least absolute shrinkage and selection operator algorithm were performed for feature selection, and a support vector machine was used for classification. The receiver operating characteristic (ROC) curve was applied to assess the model discrimination.

RESULTS

The areas under the ROC curves (AUCs) for the T1WI, T2WI, A and, P and clinical models were 0.893, 0.911, 0.958, 0.997 and 0.516 in the primary cohort, and 0.882, 0.902, 0.920, 0.962 and 0.649 in the validation cohort, respectively. All radiomics models performed better than clinical model and radiologists' evaluation both in the training and testing cohorts by comparing the AUC of various models, all P<0.050. Good calibration was achieved.

CONCLUSIONS

The radiomics models based on multiparametric MRI have the potential ability to classify PDAC and MFCP lesions.

Differentiation between non-hypervascular pancreatic neuroendocrine tumors and mass-forming pancreatitis using contrast-enhanced computed tomography

BACKGROUND

Non-hypervascular pancreatic neuroendocrine tumors (PNETs) showed slight or iso-enhancement in contrast-enhanced computed tomography (CE-CT), which shared similar imaging findings with mass-forming pancreatitis (MFPs).

PURPOSE

To explore the value of CT imaging features in differentiating the two diseases.

MATERIAL AND METHODS

Fifty-one patients with histologically proved MFPs (n = 27) or non-hypervascular PNETs (n = 24) were included. Two radiologists reviewed CT imaging findings and clinical features. Logistic regression analysis was performed to identify relevant features in differentiating non-hypervascular PNETs and MFPs. Receiver operating characteristic (ROC) curve analysis was used to show the performance of the optimal parameters in differentiating non-hypervascular PNETs and MFPs.

RESULTS

A well-defined margin was more common in non-hypervascular PNETs (P < 0.05) than that in MFPs. MFPs often occurred in older people (P < 0.01) and the head-neck of the pancreas compared with non-hypervascular PNETs (P < 0.05). Metastases only presented in non-hypervascular PNETs (P < 0.05). CT values at venous phase and delay phase of MFPs were higher (P = 0.010 and P = 0.029) than those in non-hypervascular PNETs. Logistic analysis showed gender, tumor margin, CT values at venous phase, and tumor components were independent predictors in differentiating the two lesions. The area under the curve (AUC) was 0.938 with a sensitivity of 87.5% and specificity of 92.6% for combined predicators.

CONCLUSION

Gender, tumor margin, CT values at venous phase, and tumor components were useful predicators in differentiating non-hypervascular PNETs and MFPs.

CT severity indices derived from low monoenergetic images at dual-energy CT may improve prediction of outcome in acute pancreatitis

OBJECTIVES

To determine whether imaging features and severity indices using low monoenergetic DECT images improve diagnostic conspicuity and outcome prediction in acute pancreatitis compared to conventional images.

METHODS

A retrospective study of patients with clinical and radiographic signs of acute pancreatitis who underwent 50 contrast-enhanced CT exams conducted on a single-source DECT was performed. Representative conventional and 50 keV-monoenergetic images were randomized and presented to four abdominal radiologists to determine preferred imaging for detecting fat stranding and parenchymal inflammation. Contrast and signal-to-noise ratios were constructed for necrotic, hypoattenuated, inflamed, and healthy parenchyma. These parameters and the CT severity index (CTSI) were compared between conventional and low monoenergetic images using paired t tests and correlated to clinical outcome.

RESULTS

Although preference for conventional images was noted for subtle peri-pancreatic fat stranding (169/200 (85%) reads), there was clear preference for low monoenergetic images among all readers for pancreatic inflammation evaluation (188/200 (94%) reads). Moreover, identification of small, hypoattenuating inflammatory foci on monoenergetic images alone in 13/50 (26%) cases resulted in upstaged CTSI from mild to moderate in 7/50 (14%), associated with longer hospitalization (16 ± 17 days vs. 5 ± 2 days; p < 0.05), ICU admission, and drainage. Quantitatively, a twofold difference between normal and inflamed parenchyma attenuation was identified for monoenergetic (44.8 ± 27.6) vs. conventional (25.1 ± 14.7) images (p < 0.05). Significant increases were seen in the monoenergetic SNR and CNR compared to the conventional images (p < 0.05).

CONCLUSIONS

DECT low monoenergetic images afford better tissue assessment and demarcation of inflamed pancreatic parenchyma. Additionally, they provide improved characterization of the extent parenchymal necrosis, enabling better classification that may better predict severe clinical outcomes.

KEY POINTS

• DECT low monoenergetic images afford better tissue assessment and demarcation of inflamed pancreatic parenchyma and provide improved characterization of the extent parenchymal necrosis. • Qualitatively, low monoenergetic images were preferred over conventional DECT images for the evaluation of pancreatic inflammation; and quantitatively, there is a twofold difference between normal and inflamed parenchyma attenuation, SNR, and CNR between monoenergetic vs. conventional images. • Monoenergetic imaging identified additional small, hypoattenuating inflammatory foci in 26% resulting in an upstaged CT severity index in 14% associated with longer hospitalization, ICU admission, and drainage, thereby enabling better classification and better prediction of severe clinical outcomes.

Clinical value of energy spectrum curves of dual-energy computer tomography may help to predict pathological grading of gastric adenocarcinoma

BACKGROUND

To explore the clinical value of energy spectrum curves of dual-energy computer tomography (CT) in quantitative evaluation of different pathological grades of gastric adenocarcinoma.

METHODS

A total of 62 patients with 36 poorly, 25 moderately and 1 well differentiated gastric adenocarcinomas confirmed pathologically were collected. Dual-energy CT plain and enhanced scanning were undergone before operation. Dual-Energy software was used to measure the slope of the energy spectrum curves (λ) in arterial and venous phases (VPs) after image reconstruction. Patients were divided into two groups according to the pathological results, including well and moderately differentiated gastric adenocarcinoma group and poorly differentiated gastric adenocarcinoma group. Data of each group were analyzed by independent sample t-test. Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficiency of the corresponding parameters.

RESULTS

There were significant differences in λ values of 40-50, 40-60, 40-80, 40-90, 40-100, 40-120, 40-130, 40-140 and 40-150 keV energy ranges in VP between the well and moderately differentiated group and poorly differentiated group (P<0.05), but no significant differences in λ values of different energy ranges in arterial phase (AP) between the two groups (P>0.05). And the area under curve in 40-120 keV energy range was the largest in VP. λ_40-120keV=2.69 was selected as the diagnostic threshold with the maximum Youden index, the sensitivity and specificity were 61.1% and 76%, respectively.

CONCLUSIONS

The energy spectrum curve of dual-energy CT had certain diagnostic value in the quantitative evaluation of pathological grading of gastric adenocarcinoma.

New advances in CT imaging of pancreas diseases: a narrative review

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Value of multidisciplinary collaboration in acute and chronic pancreatitis

Multidisciplinary collaboration (MDC) has been widely adopted in healthcare to optimize patient care. MDC brings several specialized healthcare providers to the table using several methods, including multidisciplinary meetings (MDMs), multidisciplinary clinics, teleconferences, and online multidisciplinary expert panels, to reach the goal of achieving the best diagnosis and treatment plan for complex diseases. Diagnosis and management of acute/chronic pancreatitis is complex which necessitates the development and utilization of MDC. The key members of pancreatitis MDM include gastroenterologists, radiologists, pathologists, hepatobiliary surgeons, chairperson, and a coordinator. After selection of admitted or referred patients, the availability of required information is reviewed, and then each case is discussed. The final diagnosis and treatment plan is confirmed by consensus, especially for complex cases that require endoscopic intervention or pancreatectomy and patients with the possibility of pancreatic adenocarcinoma. It has been shown that MDMs have improved the clinical outcome of patients with acute/chronic pancreatitis. In addition to MDM, the feasibility of multidisciplinary clinics, teleconferences, and online multidisciplinary expert panels for the management of pancreatic disorders has been investigated. Understanding structure, potential advantages, and limitations of MDC will help clinicians and healthcare systems in developing an optimized MDC to improve the management of acute/chronic pancreatitis. This narrative review summarized prior recommendations and explored the impact of MDC on clinical outcomes of patients with pancreatitis. Our recommendations offer a generalizable method that can be utilized by healthcare systems.

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.

Advanced imaging techniques for chronic pancreatitis

MRI and MRCP play an important role in the diagnosis of chronic pancreatitis (CP) by imaging pancreatic parenchyma and ducts. MRI/MRCP is more widely used than computed tomography (CT) for mild to moderate CP due to its increased sensitivity for pancreatic ductal and gland changes; however, it does not detect the calcifications seen in advanced CP. Quantitative MR imaging offers potential advantages over conventional qualitative imaging, including simplicity of analysis, quantitative and population-based comparisons, and more direct interpretation of detected changes. These techniques may provide quantitative metrics for determining the presence and severity of acinar cell loss and aid in the diagnosis of chronic pancreatitis. Given the fact that the parenchymal changes of CP precede the ductal involvement, there would be a significant benefit from developing MRI/MRCP-based, more robust diagnostic criteria combining ductal and parenchymal findings. Among cross-sectional imaging modalities, multi-detector CT (MDCT) has been a cornerstone for evaluating chronic pancreatitis (CP) since it is ubiquitous, assesses primary disease process, identifies complications like pseudocyst or vascular thrombosis with high sensitivity and specificity, guides therapeutic management decisions, and provides images with isotropic resolution within seconds. Conventional MDCT has certain limitations and is reserved to provide predominantly morphological (e.g., calcifications, organ size) rather than functional information. The emerging applications of radiomics and artificial intelligence are poised to extend the current capabilities of MDCT. In this review article, we will review advanced imaging techniques by MRI, MRCP, CT, and ultrasound.

Pancreatitis and PDAC: association and differentiation

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

LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand.

METHOD

To comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI-) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites.

RESULTS

A total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity.

CONCLUSION

Bile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.

Extracellular volume fraction determined by equilibrium contrast-enhanced dual-energy CT as a prognostic factor in patients with stage IV pancreatic ductal adenocarcinoma

OBJECTIVES

To evaluate the feasibility of equilibrium contrast-enhanced dual-energy CT (DECT), as compared with single-energy CT (SECT) and to calculate extracellular volume (ECV) fraction to predict the survival outcomes of pancreatic ductal adenocarcinoma (PDAC) patients with distant metastases (stage IV) treated with chemotherapy.

METHODS

The study cohort included a total of 66 patients with stage IV PDAC who underwent DECT before systemic chemotherapy between July 2014 and March 2017. Unenhanced and 120-kVp equivalent images during the equilibrium phase were used to calculate tumor SECT-derived ECV fractions, and iodine density images were obtained from equilibrium-phase DECT for DECT-derived ECV fractions. Correlations between SECT- and DECT-derived ECV fractions were identified using the Pearson correlation coefficient and Bland-Altman analysis. The effects of clinical prognostic factors and tumor SECT- and DECT-derived ECV fractions on progression-free survival (PFS) and overall survival (OS) were assessed by univariate and multivariate analyses using Cox proportional hazards models.

RESULTS

The correlation between SECT- and DECT-derived ECV fractions was strong (r = 0.965; p < 0.001). The Bland-Altman plot between SECT- and DECT-derived ECV fractions showed a small bias (- 3.4%). Increasing tumor SECT- and DECT-derived ECV fractions were associated with a positive effect on PFS (SECT, p = 0.002; DECT, p = 0.007) and OS (DECT, p = 0.014; DECT, p = 0.015). Only tumor DECT-derived ECV fraction was an independent predictor of PFS (p = 0.018) and OS (p = 0.022) in patients with stage IV PDAC treated with chemotherapy on multivariate analysis.

CONCLUSIONS

The ECV fraction determined by equilibrium contrast-enhanced DECT can potentially predict the survival of patients with stage IV PDAC treated with chemotherapy.

KEY POINTS

• Extracellular volume fraction of stage IV pancreatic ductal adenocarcinoma determined by dual-energy CT was strongly correlated to that with single-energy CT (r = 0.965, p < 0.001). • Tumor extracellular volume fraction was an independent predictor of progression-free survival (p = 0.018) and overall survival (p = 0.022). • Extracellular volume fraction determined by dual-energy CT could be a useful imaging biomarker to predict the survival of patients with stage IV pancreatic ductal adenocarcinoma treated with chemotherapy.

Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors

Multidetector computed tomography (MDCT) is a widely used cross-sectional imaging modality for initial evaluation of patients with suspected pancreatic ductal adenocarcinoma (PDAC). However, diagnosis of PDAC can be challenging due to numerous pitfalls associated with image acquisition and interpretation, including technical factors, imaging features, and cognitive errors. Accurate diagnosis requires familiarity with these pitfalls, as these can be minimized using systematic strategies. Suboptimal acquisition protocols and other technical errors such as motion artifacts and incomplete anatomical coverage increase the risk of misdiagnosis. Interpretation of images can be challenging due to intrinsic tumor features (including small and isoenhancing masses, exophytic masses, subtle pancreatic duct irregularities, and diffuse tumor infiltration), presence of coexisting pathology (including chronic pancreatitis and intraductal papillary mucinous neoplasm), mimickers of PDAC (including focal fatty infiltration and focal pancreatitis), distracting findings, and satisfaction of search. Awareness of pitfalls associated with the diagnosis of PDAC along with the strategies to avoid them will help radiologists to minimize technical and interpretation errors. Cognizance and mitigation of these errors can lead to earlier PDAC diagnosis and ultimately improve patient prognosis.

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.

Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography

PURPOSE

Both chronic mass-forming pancreatitis (CMFP) and pancreatic ductal adenocarcinoma (PDAC) are focal pancreatic lesions and share very similar clinical symptoms and imaging performance. There is great clinical value in preoperative differentiation of those two lesions. The purpose of this study was to investigate the value of computed tomography (CT) features in discriminating CMFP from PDAC.

PATIENTS AND METHODS

Forty-seven patients with pathologically confirmed PDAC and 21 patients with CMFP were included in this study. Demographic and CT features, including tumor location, size, margin, pancreatic or bile duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement, were retrospectively analyzed and compared. Multivariate logistic regression analyses were adopted to identify relevant CT imaging features to discriminate CMFP from PDAC.

RESULTS

There were significant differences between CMFP and PDAC with respect to main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, and tumor contrast enhancement. Delayed contrast enhancement (>70.5 Hounsfield units) showed high sensitivity and specificity of 84.2% and 84.7%. The areas under the curve (AUCs) of the predicting models based on qualitative and quantitative variables were 0.770 (95% CI: 0.660-0.880) and 0.943 (95% CI: 0.888-0.999), respectively. When all significant variables were used in combination to build a predicting model, the AUC was 0.969 (95% CI: 0.930-1.000) with 84.2% sensitivity and 94.7% specificity.

CONCLUSION

Main pancreatic duct dilatation, vascular invasion, cystic necrosis, pancreatic atrophy, calcification, tumor size, and tumor contrast enhancement were shown to be useful CT imaging features in discriminating CMFP from PDAC.

Correlation Between Dual-Energy Computed Tomography Single Scan and Computed Tomography Perfusion for Pancreatic Cancer Patients: Initial Experience

OBJECTIVE

The objective of this study was to evaluate the role and limit of iodine maps by dual-energy computed tomography (CT) single scan for pancreatic cancer.

METHODS

Thirty patients with suspected solitary pancreatic cancer were enrolled in this study and underwent CT perfusion and iodine maps. The parameters of pancreatic cancer and normal pancreatic tissue were calculated. Pearson correlation and paired t test were used for evaluating 2 techniques.

RESULTS

Iodine concentration had a moderate positive correlation with blood flow or blood volume (P < 0.05 for both). All values of iodine concentration and blood flow, iodine concentration, and blood volume had significant positive correlations (P < 0.001 for both). The mean effective dose for CT perfusion and iodine maps had significant difference (8.61 ± 0.00 mSv vs 1.13 ± 0.14 mSv, P < 0.001).

CONCLUSIONS

Iodine maps had the potential to replace routine CT perfusion for pancreatic cancer with low radiation dose.

Dual-Energy CT of the Pancreas

This article explores the technical background of dual-energy CT (DECT) imaging along with its basic principles, before turning to a review of the various DECT applications specific to pancreatic imaging. In light of the most recent literature, we will review the constellation of DECT applications available for pancreatic imaging in both oncologic and non-oncologic applications. We emphasize the increased lesion conspicuity and the improved tissue characterization available with DECT post-processing tools. Finally, future clinical applications and opportunities for research will be overviewed.

Imaging in chronic pancreatitis: State of the art review

Chronic pancreatitis (CP) is an important gastrointestinal cause of morbidity worldwide. It can severely impair the quality of life besides life-threatening acute and long-term complications. Pain and pancreatic exocrine insufficiency (leading to malnutrition) impact the quality of life. Acute complications include pseudocysts, pancreatic ascites, and vascular complications. Long-term complications are diabetes mellitus and pancreatic cancer. Early diagnosis of CP is crucial to alter the natural course of the disease. However, majority of the cases are diagnosed in the advanced stage. The role of various imaging techniques in the diagnosis of CP is discussed in this review.

Differentiation between malignant and benign rectal tumors by dual-energy computed tomography - a feasibility study

INTRODUCTION

To assess the performance of Dual Energy Computed Tomography (DECT) in the differentiation between benign and malignant tumors in the rectum.

MATERIAL AND METHODS

We enrolled 8 subjects with rectal tumors suspected to be an early rectal cancer during colonoscopy. All subjects underwent Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Endorectal Ultrasound (ERUS) for staging. Furthermore, all subjects underwent fast switching of tube voltage between 80 and 140 kVp DECT of the pelvis. The 8 subjects had histopathological verified benign adenomas after transanal endoscopic microsurgery resection (TEM). The 8 subjects were matched with 8 consecutively selected subjects with histopathologically verified malignant rectal tumors. The DECT images were analyzed to assess the difference between malignant and benign rectal tumors. All DECT images were reviewed by experienced radiologists. In each DECT scanning, we applied three regions of interest (ROIs) for the acquisition of the DECT unique quantitative parameters. The mean atomic mass (effective Z value), iodine concentration, dual energy ratio (DER) and dual-energy index (DEI) was determined in both groups.

RESULTS

The comparison of the 2 groups showed a significant difference in effective Z and a nonsignificant difference regarding iodine concentration, DER, and DEI in the two groups.

CONCLUSION

Dual-energy CT demonstrated a difference in the mean atomic mass in benign colorectal tumors in comparison to malignant colorectal tumors.

Discriminating chronic pancreatitis from pancreatic cancer: Contrast-enhanced EUS and multidetector computed tomography in direct comparison

BACKGROUND AND OBJECTIVES

To compare the ability of multidetector computed tomography (MDCT) and contrast-enhanced EUS to discriminate chronic pancreatitis (CP) from pancreatic ductal adenocarcinoma (PDAC).

SUBJECTS AND METHODS

A total of 215 patients (age: 62 ± 15 years, sex: f/m 80/135) were included in this retrospective study. All patients were examined by conventional endoscopic B-mode and contrast-enhanced high mechanical index EUS (CEHMI-EUS). CELMI-EUS was performed in 159 patients and endoscopic sonoelastography (ESE) in 210 patients. MDCT was carried out in 131 patients as part of their clinical work-up. Radiological reports were retrospectively analyzed. Final diagnosis was achieved by biopsy and evaluation of cytological specimens collected was performed by EUS-FNA, surgery, or follow-up of 12 months or more in patients with benign findings. In a subgroup of 100 patients, all diagnostic five methods were performed, and head-to-head analysis was performed.

RESULTS

Sensitivity and specificity for MDCT were 89% and 70% and for CEHMI-EUS were 96% and 91%, respectively. Sensitivities and specificities for EUS were 92% and 63% for B-Mode EUS, 96% and 38% for ESE, and 82% and 76% for CELMI-EUS, respectively. In the head-to-head analysis, each modality had shown lower numbers for specificity than shown in the overall group analysis because of high drop-out rate. EUS-FNA for PDAC had a sensitivity of 96% and a specificity of 100%.

CONCLUSIONS

Contrast-enhanced EUS is a reliable tool in discriminating PDAC from CP.

Functional imaging in pancreatic disease

In addition to the classical morphological evaluation of pancreatic disease, the constant technological advances in imaging techniques based fundamentally on computed tomography and magnetic resonance imaging have enabled the quantitative functional and molecular evaluation of this organ. In many cases, this imaging-based information results in substantial changes to patient management and can be a fundamental tool for the development of biomarkers. The aim of this article is to review the role of emerging functional and molecular techniques based on computed tomography and magnetic resonance imaging in the evaluation of pancreatic disease.

Practical Applications of Dual-Energy Computed Tomography in the Acute Abdomen

With new developments in workflow automation, as well as technological advances enabling faster imaging with improved image quality and dose profile, dual-energy computed tomography is being used more often in the imaging of the acutely ill and injured patient. Its ability to identify iodine, differentiate it from hematoma or calcification, and improve contrast resolution has proven invaluable in the assessment of organ perfusion, organ injury, and inflammation.

The Role of Dual-Energy Computed Tomography in Assessment of Abdominal Oncology and Beyond

The added value and strength of dual energy computed tomography for the evaluation of oncologic patients revolve around the use of lower energy reconstructed images and iodine material density images. Lower keV simulated monoenergetic images optimize soft tissue tumor to nontumoral attenuation differences and increase contrast to noise ratios to improve lesion detection. Iodine material density images or maps are helpful from a qualitative standpoint for image interpretation because they result in improved detection and characterization of tumors and lymph node involvement, and from a quantitative assessment by enabling interrogation of specific properties of tissues to predict and assess therapeutic response.

Diagnostic value of single-source dual-energy spectral computed tomography in differentiating parotid gland tumors: initial results

Background

An accurate preoperative diagnosis that helps distinguish between benign and malignant parotid gland tumors is very important because the results strongly affect surgical procedures. We aimed to evaluate the value of single-source dual-energy computed tomography (ssDECT) in differentiating malignant from benign parotid gland tumors.

Methods

Fifty patients underwent enhanced neck ssDECT scanning before surgery. The images were analyzed using the gemstone spectral imaging (GSI) viewer software.

Results

Fifty-two tumors (43 patients) were confirmed histopathologically, comprising of 12 pleomorphic adenomas (PAs), 24 Warthin tumors (WTs) (15 patients), and 16 malignant tumors (MTs). The iodine concentration (IC), normalized iodine concentration to common carotid artery (NIC_A) and slope value of the spectral curve (λ_HU) of the WTs were significantly higher than those of MTs and PAs (all P<0.05). The optimal IC, NIC_A and λ_HU thresholds for differentiating PAs from MTs were 0.91 mg/mL, 0.15 and 1.09, respectively, achieving sensitivities of 91.7%, 91.7% and 91.7%, specificities of 95.0%, 85.0% and 95.0%, and accuracies of 94.2%, 86.5% and 94.2%, respectively for distinguishing PAs from MTs. The optimal IC, NIC_A and λ_HU thresholds for distinguishing WTs from MTs were 1.46 mg/mL, 0.20 and 1.72, achieving sensitivities of 91.7%, 95.8% and 91.7%, and specificities of 89.3%, 85.7% and 89.3%, respectively. The accuracy was 90.4%, 90.4% and 90.4%, respectively.

Conclusions

The parameters of ssDECT in enhanced CT scans are useful in the differential diagnosis of parotid tumors.

Advances in Pancreatic CT Imaging

OBJECTIVE

The purpose of this article is to discuss the advances in CT acquisition and image postprocessing as they apply to imaging the pancreas and to conceptualize the role of radiogenomics and machine learning in pancreatic imaging.

CONCLUSION

CT is the preferred imaging modality for assessment of pancreatic diseases. Recent advances in CT (dual-energy CT, CT perfusion, CT volumetry, and radiogenomics) and emerging computational algorithms (machine learning) have the potential to further increase the value of CT in pancreatic imaging.

Multi-institutional survey on imaging practice patterns in pancreatic ductal adenocarcinoma

PURPOSE

To study the practice patterns for performance and interpretation of CT/MRI imaging studies in patients with pancreatic ductal adenocarcinoma (PDAC) at multiple institutions using a survey-based assessment.

METHODS

In this study, abdominal radiologists/body imagers on the Society of Abdominal Radiology disease-focused panel for PDAC and from multiple institutions participated in an online survey. The survey was designed to investigate the imaging and reporting practice patterns for PDAC. The survey questionnaire addressed the experience of referring providers, choice of imaging modality for diagnosis and follow-up of PDAC, structured imaging templates utilization for PDAC, and experiences with the use of structured reports.

RESULTS

The response rate was 89.6% (43/48), with majority of the respondents working in a teaching hospital or academic research center (95.4%). While 86% of radiologists reported use of structured reporting templates in their practice, only 60.5% used standardized templates specific to PDAC. This lower percentage was despite most of them (77%) being aware of existence of PDAC-specific templates and recognizing their benefits, such as preference by referring providers (83%), improved uniformity (100%), and higher accuracy of reports (76.2%). The common impediments to the use of PDAC-specific templates were interference with efficient workflow (67.5%), lack of interest (52.5%), and complexity of existing templates (47.5%). With regards to imaging practice, 92.7% (n = 40/43) of respondents reported performing dynamic multiphasic pancreatic protocol CT for evaluation of patients with initial suspicion or staging of PDAC.

CONCLUSION

Structured reporting templates for PDAC are not universally utilized in subspecialty abdominal/body imaging practices due to concerns of interference with efficient workflow and complexity of templates. Multiphasic pancreatic protocol CT is most frequently performed for evaluation of PDAC.

Mass forming chronic pancreatitis mimicking pancreatic cystic neoplasm: A case report

Mass forming chronic pancreatitis is very rare. Diagnosis could be done by the pathologic findings of focal inflammatory fibrosis without evidence of tumor in pancreas. A 34-year-old man presented with right upper abdominal pain for a few weeks and slightly elevated bilirubin level on clinical findings. Radiological findings of multidetector-row computed tomography, magnetic resonance (MR) imaging with MR cholangiopancreatography and endoscopic ultrasonography revealed focal branch pancreatic duct dilatation with surrounding delayed enhancing solid component at uncinate process and head of pancreas, suggesting branch duct type intraductal papillary mucinous neoplasm. Surgery was done and pathology revealed the focal chronic inflammation, fibrosis, and branch duct dilatation. Herein, I would like to report the first case report of mass forming chronic pancreatitis mimicking pancreatic cystic neoplasm.