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

The clinical applications of dual-layer spectral detector CT in digestive system diseases

OBJECTIVE

Dual-layer spectral detector CT (DLCT) has several advantages in clinical practice, this study aims to reveal the clinical applications of DLCT in digestive system diseases.

MATERIALS AND METHODS

We searched PubMed and Cochrane Reviews for articles published from January 1, 2010 to May 31, 2024, using the terms "dual-layer spectral detector CT" or "dual-layer CT" combined with "hepatic fat" or "hepatic fibrosis" "hepatocellular carcinoma" or "pancreatic ductal adenocarcinoma" or "pancreatic neuroendocrine tumors" or "gastric cancer" or "colorectal cancer" or "Crohn's disease" or "bowel ischemia" or "acute abdominal conditions".

RESULTS

DLCT consists of a top layer sensitive to lower-energy photons and a bottom layer sensitive to higher-energy photons. This configuration enables simultaneous acquisition of two energy spectra from a single X-ray beam ensuring consistent spatial alignment and temporal resolution. Spectral raw images allow image post-processing to improve image quality, reduce radiation doses and contrast media doses, and generate multiple quantitative parameters. It has broad potential for early detection, accurate staging, efficacy assessment, and prognosis prediction of liver, pancreatic, and gastrointestinal diseases, as well as for the assessment of digestive system vasculature.

CONCLUSIONS

DLCT not only provides valuable information for the clinical diagnosis and therapeutic effect evaluation of digestive system diseases but also may play a more important role in the overall management of digestive diseases and in the decision-making of individualized medicine.

KEY POINTS

Question What are the advantages of DLCT compared to traditional single-energy CT in the early detection, staging, and therapeutic evaluation of digestive system diseases? Findings DLCT enhances image quality, improves tissue characterization, and allows for multi-parametric analysis, making it superior in detecting and evaluating liver, pancreatic, and gastrointestinal diseases. Clinical relevance DLCT provides high-quality, multi-parametric imaging that improves the accuracy of diagnosing digestive diseases, facilitates more precise treatment planning, and enhances monitoring of treatment response, ultimately contributing to better patient management and prognosis.

CT Imaging of the Pancreas: A Review of Current Developments and Applications

Pancreatic cancer continues to pose daily challenges to clinicians, radiologists, and researchers. These challenges are encountered at each stage of pancreatic cancer management, including early detection, definite characterization, accurate assessment of tumour burden, preoperative planning when surgical resection is possible, prediction of tumour aggressiveness, response to treatment, and detection of recurrence. CT imaging of the pancreas has made major advances in recent years through innovations in research and clinical practice. Technical advances in CT imaging, often in combination with imaging biomarkers, hold considerable promise in addressing such challenges. Ongoing research in dual-energy and spectral photon-counting computed tomography, new applications of artificial intelligence and image rendering have led to innovative implementations that allow now a more precise diagnosis of pancreatic cancer and other diseases affecting this organ. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of CT imaging of the pancreas. By highlighting key contributions in diagnostic imaging, artificial intelligence, and image rendering, this article provides a comprehensive overview of how these innovations are enhancing diagnostic precision and improving outcome in patients with pancreatic diseases.

Diagnostic value of dual-layer spectral detector CT parameters for differentiating high- from low-grade bladder cancer

OBJECTIVES

This study aimed to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in distinguishing between low- and high-grade bladder cancer (BCa).

METHODS

This single-center retrospective study included pathologically confirmed BCa patients who underwent preoperative contrast-enhanced DLCT. Patients were divided into low- and high-grade groups based on pathology. We measured and calculated the following spectral CT parameters: iodine density (ID), normalized ID (NID), arterial enhancement fraction (AEF), extracellular volume (ECV) fraction, virtual non-contrast (VNC), slope of the attenuation curve, and Z effective (Zeff). Univariate and multivariable logistic regression analyses were used to determine the best predictive factors in differentiating between low- and high-grade BCa. We used receiver operating characteristic curve analysis to assess diagnostic performance and decision curve analysis to determine the net benefit.

RESULTS

The study included 64 patients (mean age, 64 ± 11.0 years; 46 men), of whom 42 had high-grade BCa and 22 had low-grade BCa. Univariate analysis revealed that differences in ID and NID in the corticomedullary phase, AEF, ECV, VNC, and Zeff images were statistically significant (p = 0.001-0.048). Multivariable analysis found that AEF was the best predictor of high-grade tumors (p = 0.006). With AEF higher in high-grade BCa, AEF results were as follows: area under the curve (AUC), 0.924 (95% confidence interval, 0.861-0.988); sensitivity, 95.5%; specificity, 81.0%; and accuracy, 85.9%. The cutoff valve of AEF for predicting high-grade BCa was 67.7%.

CONCLUSION

Using DLCT AEF could help distinguish high-grade from low-grade BCa.

CRITICAL RELEVANCE STATEMENT

This research demonstrates that the arterial enhancement fraction (AEF), a parameter derived from dual-layer spectral detector CT (DLCT), effectively distinguishes between high- and low-grade bladder cancer, thereby aiding in the selection of appropriate clinical treatment strategies.

KEY POINTS

This study investigated the value of dual-layer spectral detector CT in the assessment of bladder cancer (BCa) histological grade. The spectral parameter arterial enhancement fraction could help determine BCa grade. Our results can help clinicians formulate initial treatment strategies and improve prognostications.

Extracellular Volume Derived from Equilibrium CT for the Prediction of Survival Outcomes in Patients with Pancreatic Ductal Adenocarcinoma

ObjectiveTo assess the efficiency of extracellular volume (ECV) derived from equilibrium computed tomography (CT) in predicting recurrence-free survival (RFS) and overall survival (OS) after R0 resection of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study included 83 patients who underwent CT and R0 resection between January 2016 and September 2023. The pattern of tumor recurrence and prognosis were recorded for each patient. Tumor recurrence was classified into three groups: isolated local recurrence group, distant recurrence group and censored group. The associations between the CT-ECV and clinicopathological features and recurrence pattern of PDAC were evaluated by chi-squared test. Multivariable Cox proportional-hazards models were conducted to evaluate the effects of clinical factors, CT features and CT-ECV on RFS and OS.ResultsThe median RFS and OS were 10.7 and 17.1 months, respectively. On multivariate analysis, the CT-ECV and adjacent organ invasion were found to be associated with RFS (HR, 0.968, P = .017; HR, 0.453; P = .006), and only the CT-ECV was an independent prognostic factor for OS (HR, 0.968; P = .022). Low CT-ECV group was significantly associated with elevated CA19-9, larger tumor size, G3 (tumor grade) and II/III (AJCC tumor stage) (P < .05). In the recurrence pattern analysis, the CT-ECV did not exhibit an association between local recurrence and non-local recurrence groups (P = .455), while patients in the low CT-ECV group were more inclined to experience distant recurrence after curative surgery (P = .037).ConclusionsCT-ECV determined by equilibrium contrast-enhanced CT was a useful imaging biomarker for predicting distant recurrence and survival in resectable PDAC patients, which may facilitate further risk stratification and personalized care.

Innovative imaging approaches for neuroendocrine tumor characterization: Combined dual energy CT and perfusion protocol implementation

The article addresses the diagnostic value of the combined use of computed tomography (CT) perfusion and dual-energy CT (DECT) in patients with neuroendocrine tumors. It emphasizes the heterogeneity and complexity of these neoplasms, primarily affecting the gastrointestinal tract, bronchopulmonary system, and pancreas. While conventional CT is widely employed in their diagnosis, the combination of CT perfusion and dual-energy CT offers greater precision, particularly in detecting synchronous tumors and characterizing their vascularization. A clinical case of a patient with chronic abdominal symptoms, whose diagnosis was facilitated using both combined techniques, is presented. The discussion explores how CT perfusion assesses tumor vascularization and how dual-energy CT improves soft tissue differentiation, resulting in increased diagnostic accuracy. It is highlighted that this approach not only enhances detection rates but also positively impacts clinical management and healthcare costs. Therefore, the importance of considering these advanced tools in the diagnosis of neuroendocrine tumors to improve diagnostic precision and efficiency in patient care is underscored.

Quantitative parameters of dual-layer spectral detector computed tomography for evaluating differentiation grade and lymphovascular and perineural invasion in colorectal adenocarcinoma

PURPOSE

To explore the predictive value of dual-layer spectral detector CT (SDCT) quantitative parameters for determining differentiation grade, lymphovascular invasion (LVI) and perineural invasion (PNI) in colorectal adenocarcinoma (CRAC) patients.

METHODS

A total of 106 eligible patients with CRAC were included in this study. Spectral parameters, including CT values at 40 and 100 keV, the effective atomic number (Zeff), the iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and the normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP), were compared according to the differentiation grade and the status of LVI and PNI. The diagnostic accuracies of the quantitative parameters with statistical significance were determined via receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated.

RESULTS

There were 57 males and 49 females aged 43-86 (69 ± 10) years. The measured values of the spectral quantitative parameters of the CRAC were consistent within the observer (ICC range: 0.800-0.926). The 40 keV-AP, IC-AP, NIC-AP, 40 keV-VP, and IC-VP were significantly different among the different differentiation grades in the CRAC (P = 0.040, AUC = 0.673; P = 0.035, AUC = 0.684; P = 0.031, AUC = 0.639; P = 0.044, AUC = 0.663 and P = 0.035, AUC = 0.666, respectively). A statistically significant difference was observed in 40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP, and λHU-VP between LVI-positive and LVI-negative patients (P = 0.003, AUC = 0.688; P = 0.015, AUC = 0.644; P = 0.001, AUC = 0.688; P = 0.001, AUC = 0.703 and P = 0.003, AUC = 0.677, respectively). There were no statistically significant differences in the values of the spectral parameters of the PNI state of patients with CRAC (P > 0.05).

CONCLUSION

The quantitative parameters of SDCT had good diagnostic efficacy in differentiating between different grades and statuses of LVI in patients with CRAC; however, SDCT did not have value for identifying the state of PNI.

Preliminary differentiation of benign and malignant gastric wall thickening using dual-layer spectral-detector CT

BACKGROUND

Dual-layer spectral-detector computed tomography (DLCT) may have the potential to evaluate gastric wall thickening.

PURPOSE

To evaluate the efficacy of DLCT quantitative parameters in differentiating between benign and malignant thickening of the gastric wall.

MATERIAL AND METHODS

A total of 58 patients with "gastric wall thickening" who underwent multi-phase abdominal enhanced DLCT scans were included in this study. Of these patients, 33 were malignant and 25 were benign. Parameters such as iodine concentration (IC), effective atomic number (Zeff), and attenuation of the lesions were measured during the arterial phase (AP) and venous phase (VP). Binary logistic regression was employed to calculate the combined prediction probabilities. The accuracy of the DLCT parameters was assessed using receiver operating characteristic (ROC) curves.

RESULTS

The values of IC, nIC, Zeff, normalized Zeff, and attenuation in the AP and VP were significantly higher (all P < 0.05) in the malignant group compared to the benign group. The ROC curves revealed that the IC, Zeff, and attenuation in the VP exhibited high diagnostic performance, with area under the ROC curve (AUC) values of 0.864, 0.862, and 0.840, respectively. The new combination of these three factors and gastric wall thickness had an AUC of 0.884, and the sensitivity and specificity were determined to be 81.8% and 92.0%, respectively.

CONCLUSION

Spectral CT parameters, particularly the combination of gastric wall thickness, attenuation, IC, and Zeff in VP, have value in distinguishing between benign and malignant gastric wall thickening.

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.

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.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

OBJECTIVE

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.

METHODS

A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.

RESULTS

In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).

CONCLUSION

The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.

CRITICAL RELEVANCE STATEMENT

MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.

KEY POINTS

• Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.

Principles and Applications of Dual-Layer Spectral CT in Gastrointestinal Imaging

The advance in technology allows for the development of different CT scanners in the field of dual-energy computed tomography (DECT). In particular, a recently developed detector-based technology can collect data from different energy levels, thanks to its layers. The use of this system is suited for material decomposition with perfect spatial and temporal registration. Thanks to post-processing techniques, these scanners can generate conventional, material decomposition (including virtual non-contrast (VNC), iodine maps, Z-effective imaging, and uric acid pair images) and virtual monoenergetic images (VMIs). In recent years, different studies have been published regarding the use of DECT in clinical practice. On these bases, considering that different papers have been published using the DECT technology, a review regarding its clinical application can be useful. We focused on the usefulness of DECT technology in gastrointestinal imaging, where DECT plays an important role.