Multiphasic Computed Tomography Research Articles

Serial assessment of computed tomography angiography for pulmonary and systemic arteries using a reduced contrast agent dose for the diagnosis of systemic artery-to-pulmonary artery shunts

PurposeTo evaluate the diagnostic performance and feasibility of a modified computed tomography (CT) scan protocol, we performed a serial assessment of the computed tomography angiography for pulmonary artery (CTA-P) and systemic artery (CTA-S) (CTA-PS) using a reduced contrast agent dose to diagnose systemic artery-to-pulmonary artery shunts (SPSs).Materials and methodsTwenty-five patients who underwent multiphase contrast-enhanced chest CT and conventional chest angiography were included. Three image sets (CTA-P, CTA-S, and CTA-PS) were evaluated by two board-certified radiologists. The visualization of the CT image findings associated with SPSs, such as filling defects and enhancement in the pulmonary arteries, was evaluated using a 5-point scale.ResultsThe diagnostic performance (sensitivity, specificity, and accuracy) of CT imaging findings associated with SPSs in CTA-P and CTA-PS were as follows: CTA-P, 57.1%, 87.5%, and 62.0%; CTA-PS, 81.0%, 100.0%, and 84.0%. CT findings associated with SPSs in CTA-P were significantly sensitive to the CTA-PS protocol. There were no significant differences between the CTA-S and CTA-PS protocols. The area under the curve (AUC) of the CT imaging findings associated with SPSs in the CTA-P and CTA-PS groups was 0.835 and 0.911, respectively (P = 0.191). The AUC of the CT imaging findings associated with SPSs in CTA-S and CTA-PS were 0.891 and 0.926, respectively (P = 0.373).ConclusionCTA-PS using a reduced contrast agent dose protocol could improve the overall diagnostic confidence of SPSs, owing to better visualization of CT imaging findings associated with SPSs compared to individual assessments of CTA-P or CTA-S. Therefore, CTA-PS can be used as an alternative preembolization evaluation modality to conventional angiography in patients with hemoptysis suspected of having SPSs.

Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images.

(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm's ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit's Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model's clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology.

Hepatocellular Carcinoma from a Hepatologist's Perspective.

Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, represents a growing health challenge worldwide. The incidence of HCC is rising, which, in turn, has led to a corresponding increase in the associated number of deaths. HCC will become the third leading cause of cancer-related deaths in the United States by 2030. HCC usually develops in the setting of chronic liver disease. Individuals at increased risk of HCC are recommended to undergo surveillance with ultrasound every 6 months along with serum α-fetoprotein testing. Computed tomography (CT) and magnetic resonance imaging (MRI) are considered alternatives based on specific patient factors. Lesions suspicious for HCC are recommended to undergo a diagnostic testing, which includes contrast-enhanced multiphase CT or MRI and liver biopsy when findings are indeterminate. The Barcelona Clinic Liver Cancer prognosis and treatment strategy is the most used assessment for patients with HCC ( Fig. 2 ). Curative therapies include resection, liver transplantation, and ablation. Locoregional therapies, such as transarterial chemoembolization and radioembolization, can be used for patients with intermediate-stage HCC. For patients with advanced-stage HCC, systemic therapy is often used. This review aims to provide an overview of HCC from a hepatologist's perspective, including epidemiology, screening, surveillance, diagnosis, and management.

Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison

Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.

Incidental Indeterminate Renal Lesions: Distinguishing Non-Enhancing from Potential Enhancing Renal Lesions Using Iodine Quantification on Portal Venous Dual-Layer Spectral CT.

The purpose of our study is to determine a threshold for iodine quantification to distinguish definitely non-enhancing benign renal lesions from potential enhancing masses on portal venous dual-layer spectral computed tomography (CT) to reduce the need for additional multiphase CT. In this single-center retrospective study, patients (≥18 years) scanned between April 2021 and January 2023 following the local renal CT protocol were included. Exclusion criteria were patients without renal lesions, lesions smaller than 10 mm, only fat-containing lesions, abscesses or infarction, follow-up after radiofrequent ablation, wrong scan protocol, or artefacts. Scans were performed on a dual layer detector-based spectral CT (CT 7500, Philips Healthcare, Best, The Netherlands). Iodine concentration (mgI/mL) in renal lesions was determined using spectral data. Analyses were performed for all lesions and for lesions of >30 HU on portal venous CT. Enhancement on multiphase CT (≥20 ΔHU from true unenhanced (TUE) to portal venous phase (PVP) CT) was used as reference standard. To determine thresholds for iodine concentration receiver operating characteristic (ROC) curves, area under the curve (AUC) and 95% confidence intervals were calculated. To obtain thresholds for definite (non-)enhancement, 100% sensitivity with maximum specificity and 100% specificity with maximum sensitivity were noted. Data were measured using one reader. To assess interobserver agreement, a second reader performed measurements on the PVP CT scans. A total of 103 patients (62 years ± 14, 68 men) were included. We measured 328 renal lesions, 56 enhancing lesions (17%) in 38 patients and 272 non-enhancing lesions (83%) in 86 patients. The threshold for non-enhancing lesions was 0.76 mgI/mL or lower (100% sensitivity, 76% specificity). The threshold for a definite enhancing mass was 1.69 mgI/mL or higher (100% specificity, 78% sensitivity). A total of 77% of indeterminate lesions (>30 HU on PVP CT) in our study could be definitely characterized. Renal lesions can be definitively classified as non-enhancing or enhancing on PVP spectral CT using thresholds of 0.76 mgI/mL or 1.69 mgI/mL, respectively, eliminating the need for multiphase imaging.

Segmental analysis of liver cirrhosis with different etiologies: a study based on iodine mixed imaging in port-venous phase.

Computed tomography (CT) in port-venous phase can display the intra-hepatic vessels, and may provide the possibility for segment function evaluation for cirrhosis. To assess the value of iodine mixed imaging of dual-source dual-energy CT in port-venous phase in segmental evaluation of liver cirrhosis with different etiologies. Patients diagnosed with liver cirrhosis were enrolled. Patients without cirrhosis were included as a control group. Each patient underwent iodine-contrast enhanced multi-phase dual-energy CT scanning. Parameters were analyzed by SPSS, version 22.0, and Medcalc. In total, 256 patients were investigated, including 114 Child-Pugh A, 51 Child-Pugh B, 41 Child-Pugh C and 50 control patients. Total iodine content (ICt)/body surface area (BSA) in the cirrhosis group was significantly lower than the control group (P < 0.05) and the standardized-iodine parameter (SI) of each segment decreased with cirrhosis progression. In Child-Pugh A and B, SI increased more significantly in the caudal and lateral segment in A (alcholism) than in the V (virus-related) and N (non-alcoholic steatohepatitis) groups (P < 0.001). ICt/BSA showed the best diagnosis power of cirrhosis with an area under the curve of 0.765, sensitivity of 76.0% and specificity of 71.8%. Blood flow compensated in the left lateral and caudal lobe in the early stage of liver cirrhosis. The compensation in alcoholism in the middle and early stages is significantly higher than that of V and N cirrhosis. Iodine mixed imaging in portal phase may provide the possibility of an incremental value in segmented blood flow perfusion and functional evaluation of liver cirrhosis on a morphological basis.

Deep learning to predict respiratory lung-tissue displacement from multi-phase pulmonary computed tomography images

Lung surgery, particularly lung puncture, is challenging because respiration motion significantly displaces lung tissues (e.g., blood vessels, trachea), including targeted lesions. To improve the success of lung surgery, it is essential to determine how lung tissues move during respiration. Early work used simple mathematical models (e.g., linear models) to predict lung-tissue displacement. However, the quality and quantity of pulmonary computed tomography (CT) images used to build such models were limited, preventing their universal application. Moreover, such mathematical models are too simple to describe how lung tissues are displaced during respiration. In this work, we analyzed the relationship between body-surface and lung-tissue displacement in a large number of multi-phase pulmonary CT images to create a model predicting lung-tissue displacement. First, a systematic method based on unsupervised learning approaches was designed to calculate the displacement of the body surface and lung tissues. Second, a deep neural network model was developed to predict lung-tissue displacement, using the body-surface displacement observed in CT imaging data as input. Multi-phase pulmonary CT images from 199 patients were collected to train and test our model. The validation experiment demonstrated that the prediction accuracy based on our deep learning model reached 79.8%, which outperformed traditional machine learning approaches like the linear model (79.2%), linear support vector regression model (79.2%), and support vector regression model with radial basis function (67.2%). These results demonstrated that our method could predict lung-tissue displacement based on body-surface displacement during respiration, potentially improving the success of lung puncture procedures.

Stiffness matters: Improved failure risk assessment of ascending thoracic aortic aneurysms

ObjectivesRupture and dissection are feared complications of ascending thoracic aortic aneurysms caused by mechanical failure of the wall. The current method of using the aortic diameter to predict the risk of wall failure and to determine the need for surgical resection lacks accuracy. Therefore, this study aims to identify reliable and clinically measurable predictors for aneurysm rupture or dissection by performing a personalized failure risk analysis, including clinical, geometrical, histologic, and mechanical data. MethodsThe study cohort consisted of 33 patients diagnosed with ascending aortic aneurysms without genetic syndromes. Uniaxial tensile tests until failure were performed to determine the wall strength. Material parameters were fitted against ex vivo planar biaxial data and in vivo pressure–diameter relationships at diastole and systole, which were derived from multiphasic computed tomography (CT) scans. Using the resulting material properties and in vivo data, the maximal in vivo stress at systole was calculated, assuming a thin-walled axisymmetric geometry. The retrospective failure risk was calculated by comparing the peak wall stress at suprasystolic pressure with the wall strength. ResultsThe distensibility coefficient, reflecting aortic compliance and derived from blood pressure measurements and multiphasic CT scans, outperformed predictors solely based on geometrical features in assessing the risk of aneurysm failure. ConclusionsIn a clinical setting, multiphasic CT scans followed by the calculation of the distensibility coefficient are of added benefit in patient-specific, clinical decision-making. The distensibility derived from the aneurysm volume change has the best predictive power, as it also takes the axial stretch into account.

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

ObjectivesPosthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients.MethodsA total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test.ResultsCT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts.ConclusionsThe novel model contributes to the preoperative prediction of PHLF in resectable HCC patients.Critical relevance statementThe novel model combined CT–derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin–bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma.Key points• CT-derived ECV correlated well with the fibrosis stage of the background liver.• CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC.• The AUC of the model was higher than the CT-derived ECV and mFLR ratio.• The model showed a superior predictive performance than that of the ALBI score.Graphical

Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma

Introduction: Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. Results: The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. Conclusion: Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.

Hemodynamic Characteristics of Intracranial Atherosclerotic Stenosis: A Pilot Study of Contrast Enhancement Time-Density Curves Based on Regions of Interest.

The present study aimed to analyze the hemodynamic characteristics of occluded vessels responsible for acute ischemic stroke and to diagnose the occlusion types. Multimodal computed tomography (CT) was used to accurately identify the range of occlusion of large intracranial vessels. Regions of interest (ROI 1-3 ) were manually delineated at sites 2 mm away from the proximal, middle, and distal portions of each occlusion, generating 3 contrast enhancement time-density curves. The peak CT attenuation values, or Hounsfield units (H 1-3 ), and time-to-peak values (T 1-3 ) were extracted from each curve. H 0 and T 0 of the time-density curve, based on ROI 0 of the automatically recognized input artery, were used as the baseline values with which the odds ratios of each parameter, H 1-3/0 and T 1-3/0 , were obtained. The present study aimed to establish prediction models for intracranial atherosclerotic stenosis (ICAS) based on each ROI's time-density curve. Among the 33 acutely occluded intracranial vessels, 10 were found to have ICAS, whereas 23 did not, based on the diagnostic criteria. Significant differences were observed in patient sex, neutrophil-to-lymphocyte ratio upon admission, Alberta Stroke Program Early CT Score 24-48 hours after reperfusion therapy, and H 1/0 , H 3/0 , and T 3/0 between the ICAS and non-ICAS groups ( P < 0.05). The prediction model (model 3) based on the ROI 3 time-density curve showed the best performance for the diagnosis of ICAS (area under the curve, 0.944; 95% confidence interval, 0.854-1.000). The prediction models based on ROI 1 (model 1) and ROI 2 (model 2) showed moderate diagnostic performance (area under the curve, 0.817 vs 0.822, respectively). The best visualization for proximal occlusions was in the first phase (arterial phase) of multiphase CT angiography, and in the second phase (early venous phase) for distal occlusions. The contrast enhancement time-density curves of the ROIs at all evaluated portions of the acute ischemic stroke occlusions provided a visual display of the blood flow characteristics of the responsible vessels. The time-density curve of the ROI placed 2 mm from the distal occlusion was a combined effect of residual blood flow and collateral establishment, thus providing good performance for the diagnosis of ICAS.

A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation.

Multi-phase computed tomography (CT) images have gained significant popularity in the diagnosis of hepatic disease. There are several challenges in the liver segmentation of multi-phase CT images. (1) Annotation: due to the distinct contrast enhancements observed in different phases (i.e., each phase is considered a different domain), annotating all phase images in multi-phase CT images for liver or tumor segmentation is a task that consumes substantial time and labor resources. (2) Poor contrast: some phase images may have poor contrast, making it difficult to distinguish the liver boundary. In this paper, we propose a boundary-enhanced liver segmentation network for multi-phase CT images with unsupervised domain adaptation. The first contribution is that we propose DD-UDA, a dual discriminator-based unsupervised domain adaptation, for liver segmentation on multi-phase images without multi-phase annotations, effectively tackling the annotation problem. To improve accuracy by reducing distribution differences between the source and target domains, we perform domain adaptation at two levels by employing two discriminators, one at the feature level and the other at the output level. The second contribution is that we introduce an additional boundary-enhanced decoder to the encoder-decoder backbone segmentation network to effectively recognize the boundary region, thereby addressing the problem of poor contrast. In our study, we employ the public LiTS dataset as the source domain and our private MPCT-FLLs dataset as the target domain. The experimental findings validate the efficacy of our proposed methods, producing substantially improved results when tested on each phase of the multi-phase CT image even without the multi-phase annotations. As evaluated on the MPCT-FLLs dataset, the existing baseline (UDA) method achieved IoU scores of 0.785, 0.796, and 0.772 for the PV, ART, and NC phases, respectively, while our proposed approach exhibited superior performance, surpassing both the baseline and other state-of-the-art methods. Notably, our method achieved remarkable IoU scores of 0.823, 0.811, and 0.800 for the PV, ART, and NC phases, respectively, emphasizing its effectiveness in achieving accurate image segmentation.

Automated CT LI-RADS v2018 scoring of liver observations using machine learning: A multivendor, multicentre retrospective study

Assessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists. High-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n= 84 lesions) and a test set (n= 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard. A total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62-0.72) and 0.91 (95% CI, 0.87-0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers). Quantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist's visual analysis in patients at high-risk for HCC. Assessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist-artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist's visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.

Improving clinical suspicion of acute mesenteric ischemia among patients with acute abdomen: a cross-sectional study from an intestinal stroke center

BackgroundEarly diagnosis of acute mesenteric ischemia (AMI) is essential for a favorable outcome. Selection of patients requiring a dedicated multiphasic computed tomography (CT) scan remains a clinical challenge.MethodsIn this cross-sectional diagnostic study conducted from 2016 to 2018, we compared the presentation of AMI patients admitted to an intestinal stroke center to patients with acute abdominal pain of another origin admitted to the emergency room (controls).ResultsWe included 137 patients—52 with AMI and 85 controls. Patients with AMI [median age: 65 years (interquartile range 55–74)] had arterial and venous AMI in 65% and 35% of cases, respectively. Relative to controls, AMI patients were significantly older, more likely to have risk factors or a history of cardiovascular disease, and more likely to present with sudden-onset and morphine-requiring abdominal pain, hematochezia, guarding, organ dysfunction, higher white blood cell and neutrophil counts, and higher plasma C-reactive protein (CRP) and procalcitonin concentrations. On multivariate analysis, two independent factors were associated with the diagnosis of AMI: the sudden-onset (OR = 20, 95%CI 7–60, p < 0.001) and the morphine-requiring nature of the acute abdominal pain (OR = 6, 95%CI 2–16, p = 0.002). Sudden-onset and/or morphine-requiring abdominal pain was present in 88% of AMI patients versus 28% in controls (p < 0.001). The area under the receiver operating characteristic curve for the diagnosis of AMI was 0.84 (95%CI 0.77–0.91), depending on the number of factors.ConclusionsSudden onset and the need for morphine are suggestive of AMI in patients with acute abdominal pain and should prompt multiphasic CT scan including arterial and venous phase images for confirmation.

Collateral circulation status-guided mechanical thrombectomy in pediatric stroke with an extended ghost infarct core: illustrative case.

Mechanical thrombectomy (MT) has been proved to be a highly effective therapy to treat acute ischemic stroke due to large vessel occlusion. Often, the ischemic core extent on baseline imaging is an important determinant for endovascular treatment eligibility. However, computed tomography (CT) perfusion (CTP) or diffusion-weighted imaging may overestimate the infarct core on admission and, consequently, smaller infarct lesions called "ghost infarct cores." A 4-year-old, previously healthy boy presented with acute-onset, right-sided weakness and aphasia. Fourteen hours after the onset of symptoms, the patient presented with a National Institutes of Health Stroke Scale (NIHSS) score of 22, and magnetic resonance angiography demonstrated a left middle cerebral artery occlusion. MT was not considered because of a large infarct core (infarct core volume: 52 mL; mismatch ratio 1.6 on CTP). However, multiphase CT angiography indicated good collateral circulation, which encouraged MT. Complete recanalization was achieved via MT at 16 hours after the onset of symptoms. The child's hemiparesis improved. Follow-up magnetic resonance imaging was nearly normal and showed that the baseline infarct lesion was reversible, in agreement with neurological improvement (NIHSS score 1). The selection of pediatric stroke with a delayed time window guided by good collateral circulation at baseline seems safe and efficacious, which suggests a promising value of vascular window.

Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer

To develop a nomogram to predict lymphovascular invasion (LVI) in gastric cancer by integrating multiphase computed tomography (CT) radiomics and clinical risk factors. One hundred and seventy-two gastric cancer patients (121 training and 51 validation) with preoperative contrast-enhanced CT images and clinicopathological data were collected retrospectively. The clinical risk factors were selected by univariate and multivariate regression analysis. Radiomic features were extracted and selected from the arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images of each patient. Clinical risk factors, radiomic features, and integration of both were used to develop the clinical model, radiomic models, and nomogram, respectively. Radiomic features from AP (n=6), VP (n=6), DP (n=7) CT images and three selected clinical risk factors were used for model development. The nomogram showed better performance than the AP, VP, DP, and clinical models in the training and validation datasets, providing areas under the curves (AUCs) of 0.890 (95% CI: 0.820-0.940) and 0.885 (95% CI:0.765-0.957), respectively. All models indicated good calibration, and decision curve analysis proved that the net benefit of the nomogram was superior to that of the clinical and radiomic models throughout the vast majority of the threshold probabilities. The nomogram integrating multiphase CT radiomics and clinical risk factors showed favourable performance in predicting LVI of gastric cancer, which may benefit clinical practice.

An evaluation of the contribution of routine ultrasound when performed with multiphase CT in renal donor imaging assessment.

We sought to examine the contribution of routine ultrasound when performed with computed tomography in identifying exclusion criteria in potential living kidney donors. We performed a 10-year retrospective cohort study including all cases of potential renal donors at our center. For each case, the donor workup ultrasound (US) and multiphase computed tomography (MPCT) original reports and imaging were reviewed by a fellowship-trained abdominal radiologist in consultation with a transplant urologist and placed into one of 3 groups: (1) no significant US contribution, (2) US was useful to characterize an incidental finding (either US exclusive or US aided in CT interpretation) but did not impact donor eligibility, and (3) an US exclusive finding contributed to donor exclusion. A total of 432 potential live renal donors were evaluated (mean age 41, 263 women). In total, 340 (78.7%, group 1) cases had no significant US contribution. In 90 cases (20.8%, group 2), US helped to characterize one or more incidental findings but did not contribute to donor exclusion. In 1 (0.2%, group 3) case, an US exclusive finding (suspected medullary nephrocalcinosis) contributed towards donor exclusion. US provided limited contribution to renal donor eligibility decisions when performed routinely with MPCT. Routine ultrasound could potentially be omitted in the live renal donor workup, with alternative strategies including a selective approach to incorporating ultrasound and an expanded role of dual-energy CT. • Ultrasound is performed routinely with CT for renal donor assessment in some jurisdictions; however, this practice has come into question particularly with advances in dual-energy CT. • Our study found that routine use of ultrasound provided limited contribution, primarily assisting CT in characterization of benign findings with only 1/432 (0.2%) potential donors in a 10-year period excluded based in part on an ultrasound exclusive finding. • The role of ultrasound can be narrowed to a targeted approach for certain at-risk patients, and can be further reduced if dual-energy CT is utilized.

Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model’s performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system.

Substantiation of a new approach to the criteria for assessing the radiation dose of patients during computed tomography

BACKGROUND: In accordance with the requirements of the IAEA basic safety standards and the International Commission on Radiation Protection, comparing the radiation dose for patients undergoing computed tomography (CT) in diagnostic and treatment clinics with national or international DRLs is important for controlling medical radiation doses. The search for ways to improve DRLs calculations determines the relevance of such studies.&#x0D; AIM: To analyze the dependence of effective doses (EDs) in CT of different body parts on patients weight and to calculate the standard ED for the patient (70 and 80 kg).&#x0D; MATERIALS AND METHODS: CT acquisition protocols in 209 patients were single phase (SP) CT, while 114 patients underwent multi-phase (MP) CT. ED was calculated according to the normalized coefficients for each body area. The values of standard ED was calculated by data approximation using linear function of ED relatively body weight for each type CT scanner and body area scanned.&#x0D; RESULTS: The increase in ED following a CT examination was proportional to the body weight of patients. For SP and MP CT scans, the standard EDs were calculated according to all body areas. The mean ED, median ED, and DRLs (mSv) in these groups was slightly higher than standard ED (mSv) if the criterion was 70 kg and were close to standard ED if the criterion was 80 kg. These values give a basis for improving the guidelines concerning the recommended limits of radiation doses for CT in individual patients according to indications and body parts studied.&#x0D; CONCLUSIONS: In the study, a methodology for assessing and comparing the dose of CT-radiation at two hospitals in the two CT scanners, considering weight of a standard patient, is described. Our results show that the calculation and analysis of the standard ED of CT-examining areas of the body instead of mean ED and median ED help to compare the radiation exposure in different medical facilities more properly. Given the recent sharp increase in the number of CT studies, not exceeding the standard ED for patients with CT will reduce the long-term consequences in the form of oncological pathology among the population.

Prediction microvascular invasion of hepatocellular carcinoma based on tumour margin enhancing pattern in multiphase computed tomography images.

The presence of microvascular invasion of hepatocellular carcinoma has a significantly decreased outcome following hepatectomy or liver transplantation. Currently, it is still based on histological examination. Identification of microvascular invasion by using pre-operative imaging is important for the decision-making of surgeons and interventional radiologists. Aim of the study was to predict the microvascular invasion of hepatocellular carcinoma based on tumour margin enhancement of pre-operative multiphase computed tomography (CT) images. Fifty-three patients with hepatocellular carcinoma, who underwent pre-operative multiphase CT scans, were included in this study. Tumour margin enhancing patterns were analysed in the late arterial phase, portovenous phase, and delay phase. The CT features including peritumoral enhancement, arterial rim-enhancement, presence of daughter nodules, complete capsule enhancement in portovenous/delay phase, and nodular capsule enhancement in portovenous/delay phase were reviewed with calculations for sensitivity and specificity. Univariate analysis and multivariate analysis were used to identify predictive features for microvascular invasion (MVI). In the late arterial phase, peritumoral enhancement or the presence of daughter nodules were not predictors for MVI. Nodular capsule enhancement in the portovenous phase and delay phase were independent predictors for MVI with odds ratios of 29.25 and 33.09, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the portovenous phase were 69.23% and 96.86%, respectively. The sensitivity and specificity for incomplete/nodular capsule enhancement in the delay phase were 71.79% and 96.86%, respectively. Nodular capsule enhancement in the portovenous phase or delay phase was a good predictor for MVI.