Differentiation of liver abscess from liver metastasis using dual-energy spectral CT quantitative parameters

Assessment of Spectral Computed Tomography Image Quality and Detection of Lesions in the Liver Based on Image Reconstruction Algorithms and Virtual Tube Voltage

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis.

Single-Energy, Dual-Energy, and Photon-Counting Computed Tomography of the Liver: Current Development and Clinical Utility for the Assessment of Focal Liver Lesions

ABSTRACT

Advancements in computed tomography (CT) technology, particularly the emergence of dual-energy CT (DE-CT) and photon-counting detector CT (PCD-CT), can improve detection, characterization, and treatment monitoring of focal liver lesions. DE-CT, through its ability to differentiate tissues with similar densities and produce diverse datasets, has enhanced lesion visibility and diagnostic precision. PCD-CT further advances imaging with superior spatial resolution and material decomposition capabilities, offering potential for complex diagnostic scenarios. This review aimed to highlight the role of CT in hepatic imaging and its application to focal liver lesions.DE-CT improves lesion detectability using low-energy virtual monochromatic images, which enhance iodine contrast and reduce radiation and contrast agent doses. It also facilitates treatment response evaluation after locoregional therapies for hepatocellular carcinoma by quantifying biomarkers, such as the extracellular volume fraction. This review underscores the transformative impact of DE-CT and PCD-CT on liver imaging, emphasizing their complementary roles alongside magnetic resonance imaging. These innovations have paved the way for more precise diagnostics, improved treatment planning, and enhanced patient outcomes in the management of liver diseases.

Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT

OBJECTIVES

To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents.

MATERIALS AND METHODS

This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs.

RESULTS

The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance.

CONCLUSION

Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions.

CLINICAL RELEVANCE STATEMENT

The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses.

KEY POINTS

Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma.

Imaging of pancreatic ductal adenocarcinoma - An update for all stages of patient management

Background

Pancreatic ductal adenocarcinoma (PDAC) is a common and lethal cancer. From diagnosis to disease staging, response to neoadjuvant therapy assessment and patient surveillance after resection, imaging plays a central role, guiding the multidisciplinary team in decision-planning.

Review aims and findings

This review discusses the most up-to-date imaging recommendations, typical and atypical findings, and issues related to each step of patient management. Example cases for each relevant condition are presented, and a structured report for disease staging is suggested.

Conclusion

Despite current issues in PDAC imaging at different stages of patient management, the radiologist is essential in the multidisciplinary team, as the conveyor of relevant imaging findings crucial for patient care.

Dual-Energy CT in Oncologic Imaging

Dual-energy CT (DECT) is an innovative technology that is increasingly widespread in clinical practice. DECT allows for tissue characterization beyond that of conventional CT as imaging is performed using different energy spectra that can help differentiate tissues based on their specific attenuation properties at different X-ray energies. The most employed post-processing applications of DECT include virtual monoenergetic images (VMIs), iodine density maps, virtual non-contrast images (VNC), and virtual non-calcium (VNCa) for bone marrow edema (BME) detection. The diverse array of images obtained through DECT acquisitions offers numerous benefits, including enhanced lesion detection and characterization, precise determination of material composition, decreased iodine dose, and reduced artifacts. These versatile applications play an increasingly significant role in tumor assessment and oncologic imaging, encompassing the diagnosis of primary tumors, local and metastatic staging, post-therapy evaluation, and complication management. This article provides a comprehensive review of the principal applications and post-processing techniques of DECT, with a specific focus on its utility in managing oncologic patients.

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.

Spectral CT: Current Liver Applications

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

DECT Numbers in Upper Abdominal Organs for Differential Diagnosis: A Feasibility Study

Evaluating the similarity between two entities such as primary and suspected metastatic lesions using quantitative dual-energy computed tomography (DECT) numbers may be useful. However, the criteria for the similarity between two entities based on DECT numbers remain unclear. We therefore considered the possibility that a similarity in DECT numbers within the same organ could provide suitable standards. Thus, we assumed that the variation in DECT numbers within a single organ is sufficiently minimal to be considered clinically equivalent. Therefore, the purpose of this preliminary study is to investigate the differences in DECT numbers within upper abdominal organs. This retrospective study included 30 patients with data from hepatic protocol DECT scans. DECT numbers of the following parameters were collected: (a, b) 70 and 40 keV CT values, (c) slope, (d) effective Z, and (e, f) iodine and water concentration. The agreement of DECT numbers obtained from two regions of interest in the same organ (liver, spleen, and kidney) were assessed using Bland-Altman analysis. The diagnostic ability of each DECT parameter to distinguish between the same or different organs was also assessed using receiver operating characteristic analysis. The 95% limits of agreement within the same organ exhibited the narrowest value range on delayed phase (DP) CT [(c) -11.2-8.3%, (d) -2.0-1.5%, (e) -11.3-8.4%, and (f) -0.59-0.62%]. The diagnostic ability was notably high when using differences in DECT numbers on portal venous (PVP) and DP images (the area under the curve of DP: 0.987-0.999 in (c)-(f)). Using the variability in DECT numbers in the same organ as a criterion for defining similarity may be helpful in making a differential diagnosis by comparing the DECT numbers of two entities.

Unique diagnostic challenge in surgery: hepatic abscess versus malignancy

A man in his 30s had presented with a history of abdominal pain, vomiting and high-grade fever. He had tender hepatomegaly with peritonism in the upper abdomen. Investigations revealed a neutrophilic leucocytosis, and contrast-enhanced CT had shown several well-defined peripherally enhancing thick-walled cystic lesions with non-enhancing centres throughout the liver suggestive of pyogenic liver abscess, treated initially with antibiotics. However, ultrasonography-guided fine needle aspiration revealed atypical neoplastic cells, and a trucut biopsy showed squamous cell carcinoma (SCC). He developed acute shortness of breath. CT pulmonary angiogram confirmed pulmonary thromboembolism (PTE). Incidentally, here we saw a solitary nodule in the right middle lobe, probably a neoplastic lesion. His condition deteriorated rapidly secondary to PTE and died. A pathological review was positive for CK7, p40, p63 and CK19 confirming SCC. We concluded the primary was a pulmonary SCC with multiple hepatic metastases. Hepatic metastases can mimic an abscess; trucut biopsy with immunohistochemistry was critical for a definitive diagnosis.

3D variable flip angle T1 mapping for differentiating benign and malignant liver lesions at 3T: comparison with diffusion weighted imaging

BACKGROUND

Different methods have been used to improve the imaging diagnosis of focal liver lesions (FLL). Among them, magnetic resonance imaging (MRI) has received more attention since it provides significant amount of information without radiation exposure. However, atypical imaging characteristics of FLL on MRI may complicate the differential diagnosis between benign and malignant FLL. This study aimed to compare the diagnostic value of T1 mapping and diffusion-weighted imaging (DWI) for differentiating of benign and malignant FLLs.

METHODS

This retrospective study enrolled 294 FLLs, including 150 benign and 144 malignant lesions. Whole liver T1 mapping sequences were obtained before and 2 min after the administration of Gd-DTPA to acquire native T1 and enhanced T1 and ΔT1%. Additionally, DWI sequence was conducted to generate apparent diffusion coefficient (ADC) maps. These quantitative parameters were compared using one-way analysis of variance, and the diagnostic accuracy of T1 mapping and ADC for FLLs was calculated by area under the curve (AUC).

RESULTS

Significant differences were observed regarding the native T1, enhanced T1, ΔT1%, and ADC between benign and malignant FLLs. Furthermore, the sensitivity and specificity of the parameters are as follows: native T1 0.797/0.702 (cut off value 1635.5 ms); enhanced T1, 0.911/0.976 (cutoff value 339.2 ms); ΔT1%, 0.901/0.905 (cutoff value 70.8%); and ADC, 0.975/0.952 (cutoff value 1.21 × 10^-3 mm²/s). The ideal cutoff values for native T1 and ADC in identifying cyst and haemangioma were 2422.9 ms (AUC 0.990, P < 0.01) and 2.077 × 10^-3 mm²/s (AUC 0.949, P < 0.01), respectively, with a sensitivity and specificity of 0.963/1 and 0.852/0.892, respectively. ADC was significantly positively correlated with T1 and ΔT1%, and significantly negatively correlated with enhanced T1.

CONCLUSION

The 3D Variable flip angle T1 mapping technique with Gd-DTPA has a high clinical potential for identifying benign and malignant FLLs. The enhanced T1 and ΔT1% values have similar diagnostic accuracy compared with DWI in evaluating FLLs. Native T1 shows better performance than DWI in distinguishing benign liver lesions, specifically, cysts, and haemangioma.

Radiomics Study for Differentiating Focal Hepatic Lesions Based on Unenhanced CT Images

Objectives

To investigate the feasibility of computer-aided discriminative diagnosis among hepatocellular carcinoma (HCC), hepatic metastasis, hepatic hemangioma, hepatic cysts, hepatic adenoma, and hepatic focal nodular hyperplasia, based on radiomics analysis of unenhanced CT images.

Methods

452 patients with 77 with HCC, 104 with hepatic metastases, 126 with hepatic hemangioma, 99 with hepatic cysts, 24 with FNH, 22 with HA, who underwent CT examination from 2016 to 2018, were included. Radcloud Platform was used to extract radiomics features from manual delineation on unenhanced CT images. Most relevant radiomic features were selected from 1409 via LASSO (least absolute shrinkage and selection operator). The whole dataset was divided into training and testing set with the ratio of 8:2 using computer-generated random numbers. Support Vector Machine (SVM) was used to establish the classifier.

Results

The computer-aided diagnosis model was established based on radiomic features of unenhanced CT images. 27 optimal discriminative features were selected to distinguish the six different histopathological types of all lesions. The classifiers had good diagnostic performance, with the area under curve (AUC) values greater than 0.900 in training and validation groups. The overall accuracy of the training and testing set about differentiating the six different histopathological types of all lesions was 0.88 and 0.76 respectively. 34 optimal discriminative were selected to distinguish the benign and malignant tumors. The overall accuracy in the training and testing set was 0.89and 0.84 respectively.

Conclusions

The computer-aided discriminative diagnosis model based on unenhanced CT images has good clinical potential in distinguishing focal hepatic lesions with noninvasive radiomic features.

Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer

Objective

This study aims to assess the performance of machine learning (ML)-based contrast-enhanced CT radiomics analysis for predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer.

Methods

This retrospective study analyzed 83 patients with breast cancer liver metastases. Radiomics features were extracted from arterial phase, portal venous phase, and delayed phase images, respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and validation sets consisted of 58 and 25 cases. Variance threshold, SelectKBest, and LASSO logistic regression model were employed for feature selection. The ML classifiers were K-nearest-neighbor algorithm (KNN), support vector machine (SVM), XGBoost, RF, LR, and DT, and the performance of classifiers was evaluated by ROC analysis.

Results

The SVM classifier had the highest score in portal venous phase. The results were as follows: The AUC value of the poor prognosis group in validation set was 0.865, the sensitivity was 0.77, and the specificity was 0.83. The AUC value of the good prognosis group in validation set was 0.865, the sensitivity was 0.83, and the specificity was 0.77. In arterial phase, the XGBoost classifier had the highest score. The AUC value of the poor prognosis group in validation set was 0.601, the sensitivity was 0.69, and the specificity was 0.38. The AUC value of the good prognosis group in validation set was 0.601, the sensitivity was 0.38, and the specificity was 0.69. The LR classifier had the highest score in delayed phase. The AUC value of poor prognosis group in validation set was 0.628, the sensitivity was 0.62, and the specificity was 0.67. The AUC value of the good prognosis group in validation set was 0.628, the sensitivity was 0.67, and the specificity was 0.62.

Conclusion

Radiomics analysis represents a promising tool in predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer. The ROI in portal venous phase is most suitable for predicting the efficacy of anti-HER2 therapy, and the SVM algorithm model has the best efficiency.

Role of Dual-Energy Computed Tomography in Gallbladder Disease: A Review

Gallbladder diseases are common and include a spectrum ranging from benign to cancer. Imaging plays an integral role in the diagnosis and in guiding appropriate management. While most patients with gallstone (GS) diseases can be evaluated with ultrasound, those with complicated GS disease, suspicion of cancer, or staging of cancer need additional cross-sectional imaging. Computed tomography (CT) is widely available and is often the imaging test of choice following an equivocal ultrasound or negative ultrasound in patients with unexplained symptoms. Conventional CT has limited sensitivity in detecting GS or common bile duct stones. In other scenarios, including diagnosis of acute cholecystitis (AC) and characterization of gallbladder wall thickening, an increase in accuracy using novel techniques is desirable. Dual-energy computed tomography (DECT) is increasingly incorporated into clinical practice. DECT has shown promising results in the detection of cholesterol stones that otherwise go unnoticed on conventional CT. However, its role beyond GS disease has largely been unexplored. In this review, we discuss the available literature on the DECT in biliary diseases and discuss the potential applications of this technique.

Value of spectral detector computed tomography to differentiate infected from noninfected thoracoabominal fluid collections

PURPOSE

To investigate the diagnostic value of spectral detector CT (SDCT)-derived virtual non-contrast (VNC), virtual monoenergetic images (VMI) and iodine overlays (IO) for distinguishing infected from noninfected fluid collections (FC) in the chest or abdomen.

METHOD

This retrospective study included 58 patients with venous phase SDCT with 77 FC. For all included FC, microbiological analysis of aspirated fluid served as reference. For quantitative analysis, wall thickness was measured, and (ROI)-based analysis performed within the fluid, the FC's wall (if any) and the aorta. Two radiologists qualitatively evaluated visibility of wall enhancement, diagnostic confidence regarding infection of fluid collection, confidence of CT-guided drainage catheter placement and visibility of anatomical landmarks in conventional images (CI) and VNC, VMI_40keV, IO.

RESULTS

Wall thickness significantly differed between infected (n = 46) and noninfected (n = 31) FC (3.5 ± 1.8 mm vs. 1.4 ± 1.8 mm, AUC = 0.81; p < 0.05). Fluid attenuation and wall enhancement was significantly higher in infected as compared to noninfected FC in all reconstructions (p < 0.05, respectively). Highest AUC regarding A) attenuation in fluid was yielded in CI and VMI_70,80keV (0.75); B) wall enhancement in CI (0.88) followed by iodine concentration (0.86). Contrast-to-noise ratio of wall vs. fluid was highest in VMI_40keV (p < 0.05). All assessed qualitative parameters received significantly higher ratings when using spectral reconstructions vs. CI (p for all <0.05), except for visibility of wall enhancement.

CONCLUSION

Spectral reconstructions improve the assessment of infected from noninfected thoracoabdominal fluid collections and depiction of wall enhancement. Diagnostic performance of the quantitative measurements in spectral reconstructions were comparable with measurements in conventional images.

Clinical features and imaging manifestations of retinoblastoma with hepatic metastasis

Clinical data of five patients with hepatic metastases of retinoblastoma were analyzed retrospectively (two had bilateral tumors three had unilateral intraocular tumors). On computed tomography, multiple and single low-density foci were observed. Four patients had tumor remission, and one showed no response after chemotherapy. Three patients who underwent enucleation were at high risk for extensive choroidal invasion. Central nervous system and bone metastases occurred in all five patients. Neuron-specific enolase and lactate dehydrogenase levels were significantly elevated in all patients. Two patients died (not from hepatic metastasis). Three patients (one with tumor progression and two with shorter courses) are continuing treatment.

Quantitative Assessment of Radiologically Indeterminate Local Colonic Wall Thickening on Iodine Density Images Using Dual-Layer Spectral Detector CT

RATIONALE AND OBJECTIVES

To assess local colonic wall thickening (LCWT, thicknesses: >3 mm, lengths: <5 cm) quantitatively on iodine density images using dual-layer spectral detector computed tomography (DLSCT).

MATERIALS AND METHODS

This retrospective study included 80 patients who underwent both conventional contrast-enhanced CT and colonoscopy within one month. The region of interest was delineated on the chosen images with the iodine density image model. The iodine concentration (IC), normalized IC (NIC), and thickness of the colonic wall in the lesion area were compared between the pathological and nonpathological groups.

RESULTS

There were 50 patients whose area of LCWT discovered at CT scans displayed colon neoplasia at colonoscopy. The other 30 patients with LCWT on CT images showed normal appearances during colonoscopy. There was no significant difference in colonic wall thickness between the pathological and nonpathological (p> 0.05) LCWT groups. The IC and NIC of patients with colon neoplasms were significantly higher than those with nonpathologic LCWT (both p< 0.001). The ROC curve showed that when IC and NIC was 1.49 mg/mL and 0.33, the sensitivity and specificity for diagnosing colon neoplasm were 91.5% and 75.8%, 85.1% and 84.8%, respectively.

CONCLUSION

IC and NIC values from DLSCT could provide a satisfied diagnostic value to identify LCWT caused by colon neoplasia.

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.

Liver metastasis mimicking an abscess

Hepatic metastases from carcinoid tumours are typically solid, hypervascular lesions on imaging. The cystic form, mimicking an abscess, is extremely rare. We report a case of a 48-year-old female presenting with a large hepatic mass that was diagnosed as a hepatic abscess, but the ultrasound-guided biopsy showed well-differentiated grade 1 neuroendocrine tumour. CT scan of chest, abdomen and pelvis was performed, looking for the primary tumour, it revealed an endobronchial mass of the right inferior lobe. Lung biopsy by rigid bronchoscopy was taken confirming the diagnosis of a typical carcinoid tumour.

Noninvasive Staging of Liver Fibrosis Using 5-Minute Delayed Dual-Energy CT: Comparison with US Elastography and Correlation with Histologic Findings

Background Normalized iodine concentration (NIC) (ratio of iodine concentration of liver to that of aorta) of liver at delayed dual-energy CT (DECT) may reflect the amount of fibrosis based on the extent of iodine uptake. Purpose To stage liver fibrosis by using 5-minute delayed DECT and compare findings with those of transient elastography (TE), shear-wave elastography (SWE), and histologic examination. Materials and Methods This prospective study included patients with chronic liver disease who were scheduled to undergo multiphase abdominal CT and liver biopsy from January 2017 to September 2018. Fifty individuals being screened as renal donors comprised the control group. Study participants underwent TE, SWE, multiphasic DECT (including 5-minute delayed dual-energy scanning), and liver biopsy. Multiphasic DECT and SWE were performed in the control group. The NIC of the right lobe of the liver (RNIC) was compared with liver stiffness (LS) as measured with TE and SWE and with the METAVIR fibrosis stage (ranging from F0 to F4). Diagnostic performance was assessed by using areas under the receiver operating characteristic curve (AUCs). Results A total of 107 participants (mean age, 35 years ± 12 [standard deviation]; 57 men) and 50 control subjects (mean age, 47 years ± 11; 29 women) were evaluated. The RNIC showed strong correlation with METAVIR stage (Spearman ρ = 0.81, P < .001). The AUC for RNIC with each METAVIR stage ranged between 0.86 (95% CI: 0.76, 0.97) and 0.96 (95% CI: 0.92, 0.99). The cut-off value of RNIC was 0.24 (sensitivity: 85% [86 of 101 participants; 95% CI: 77%, 91%]; specificity: 83% [84 of 101 participants; 95% CI: 42%, 98%]) for stage F1 fibrosis and 0.29 (sensitivity: 84% [67 of 80 participants; 95% CI: 74%, 90%]; specificity: 81% [65 of 80 participants; 95% CI: 63%, 92%]) for stage F2 fibrosis. RNIC correlated well with LS as measured with TE and SWE (Spearman ρ = 0.60 and 0.64, respectively; P < .001). Conclusion Normalized iodine concentration of liver at 5-minute delayed dual-energy CT showed strong correlation with the histologic stages of liver fibrosis and good diagnostic performance in estimating liver fibrosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Chandarana and Shanbhogue in this issue.

Metastatic neuroendocrine tumor masquerading as liver abscesses

Fever and deranged transaminases with liver mass(es) on imaging mandates further evaluation of the mass(es) and should be followed radiologically and clinically. In the absence of a definitive diagnosis, repeat biopsy should be done.

Application of Computed Tomography Imaging in Diagnosis of Endocrine Nerve of Gastric Cancer and Nursing Intervention Effect

In this article, some parameters and characteristics of computed tomography (CT) images in patients with gastric cancer are analyzed and the application of CT images in the diagnosis of gastric cancer endocrine nerves and the impact of nursing intervention on the quality and mental state of CT images of patients with gastric cancer are discussed. First, all patients were scanned with CT, and the CT values of the normal stomach wall and all lesions at different single-energy levels were recorded separately. Second, the improved back propagation network model was applied to realize the diagnosis of gastric cancer through the analysis of various features of CT images. The effect of nursing intervention on the image quality and mental state of CT imaging of patients with gastric cancer was studied. The results show that the energy spectrum curve of CT images and the improved back propagation network model are helpful for the initial diagnosis and identification of gastric cancer. Nursing intervention has a good influence on the clinical examination, image diagnosis, and psychological state of patients with gastric cancer, and it is easy for patients to undergo image diagnosis and examination according to correct operating procedures.

Recommendations for the Management of Incidental Hepatobiliary Findings in Adults: Endorsement and Adaptation of the 2017 and 2013 ACR Incidental Findings Committee White Papers by the Canadian Association of Radiologists Incidental Findings Working Group

The Canadian Association of Radiologists Incidental Findings Working Group consists of both academic subspecialty and general radiologists and is tasked with adapting and expanding upon the American College of Radiology incidental findings white papers to more closely apply to Canadian practice patterns, particularly more comprehensively dealing with the role of ultrasound and pursuing more cost-effective approaches to the workup of incidental findings without compromising patient care. Presented here are the 2020 Canadian guidelines for the management of hepatobiliary incidental findings. Topics covered include initial assessment of hepatic steatosis and cirrhosis, the workup of incidental liver masses identified on ultrasound and computed tomography (with algorithms presented), incidental gallbladder findings (wall thickening, calcification, and polyps), and management of incidental biliary dilatation.

Establishment of a novel system for the preoperative prediction of adherent perinephric fat (APF) occurrence based on a multi-mode and multi-parameter analysis of dual-energy CT

Background

Adherent perinephric fat (APF) is evaluated preoperatively with the Mayo adhesive probability (MAP) scoring system using conventional single-form computed tomography (CT) images. An objective or quantitative indicator for predicting APF is urgently needed for clinical application.

Methods

A total of 150 patients with renal tumours who underwent laparoscopic partial nephrectomy (LPN) were retrospectively enrolled and divided into the APF group (n=100) and the non-APF group (n=50) according to surgical results. All patients underwent a renal contrast-enhanced dual-energy CT (DECT) scan. The obtained CT DICOM data were transmitted to the DECT post-processing workstation and adopted virtual non-contrast (VNC), Rho/Z Maps, and Monoenergetic Plus (mono+) modes separately to undergo a multi-parameter analysis. A logistic stepwise investigation was utilized to analyse the related risk factors. The cutoff value was determined by the Youden index. Fifty patients were prospectively enrolled to validate the constructed model. The area under the curve (AUC), sensitivity, specificity and accuracy of the model were calculated.

Results

The study demonstrated that age, sex, body mass index (BMI), smoking status, tumour diameter, exophytic status, degree of malignancy and posterior perinephric fat thickness were related to the occurrence of APF (P<0.05). Model 1 was selected with the contrast material (CM) parameter (cutoff point 0.5), model 2 was selected with the effective atomic number (Z_eff) parameter (cutoff point 6.5), and model 3 was selected with the slope K (K) parameter (cutoff point -0.95). The AUC, sensitivity, specificity and accuracy of model 1 were 0.94, 0.94, 0.93 and 0.94, respectively; for model 2, they were 0.94, 0.93, 0.93 and 0.96, respectively; and for model 3, they were 0.92, 0.92, 0.93 and 0.92, respectively.

Conclusions

Multi-mode and multi-parameter models of DECT can effectively be used to predict the occurrence of APF.