Contrast-enhanced Contrast-enhanced Computed Tomography Images Research Articles

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Contrast-enhanced computed tomography in the venous rather than the arterial phase is essential for the evaluation of the right phrenic nerve.

Preprocedural detection of the running course of the right pericardiophrenic bundles (PBs) is considered to be useful in preventing phrenic nerve (PN) injury during catheter ablation for atrial fibrillation (AF). However, previous studies using the arterial phase of contrast-enhanced computed tomography (CT) reported a relatively low right PBs detection rate. This study included 63 patients with AF who underwent catheter ablation and preoperative contrast-enhanced CT imaging of the venous and arterial phases (66.7±10.2 years; 44 male). The venous phase of contrast-enhanced CT significantly improved the detection rate of PBs compared to the arterial phase (96.8%vs. 60.3%, p<.001), and PBs were detected in the venous phase only in 23 (36.7%) patients. No significant differences were observed between the right PBs detection rate using non-contrast CT versus the arterial phase of contrast-enhanced CT (p=.37). Patients without visualization of the right PBs during the arterial phase had a higher frequency of chronic heart failure (p=.0083), lower left ventricular ejection fraction (p=.021), and a higher CHADS2 score (p=.048) than those with visualization. In five patients whose right PBs could only be detected during the venous phase of contrast-enhanced CT, the reconstructed running course of the right PBs corresponded with the PN generated by electrical high-output pacing. Contrast-enhanced CT images of the venous phase, rather than the arterial phase, are useful in detecting the right PBs, especially in patients with heart failure or reduced left ventricular ejection fraction.

Interpretable radiomics method for predicting human papillomavirus status in oropharyngeal cancer using Bayesian networks

ObjectivesTo develop a simple interpretable Bayesian Network (BN) to classify HPV status in patients with oropharyngeal cancer. MethodsTwo hundred forty-six patients, 216 of whom were HPV positive, were used in this study. We extracted 851 radiomics markers from patients’ contrast-enhanced Computed Tomography (CT) images. Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. The area under the curve (AUC) demonstrated BN model performance in 30% of the data reserved for testing. A Support Vector Machine (SVM) based method was also implemented for comparison purposes. ResultsThe Mens eX Machina (MXM) approach selected two most relevant predictors: sphericity and max2DDiameterRow. Areas under the Curves (AUC) were found 0.78 and 0.72 on the training and test data, respectively. When using support vector machine (SVM) and 25 features, the AUC was found 0.83 on the test data. ConclusionsThe straightforward structure and power of interpretability of our BN model will help clinicians make treatment decisions and enable the non-invasive detection of HPV status from contrast-enhanced CT images. Higher accuracy can be obtained using more complex structures at the expense of lower interpretability. Advances in KnowledgeRadiomics is being studied lately as a simple imaging data based HPV status detection technique which can be an alternative to laboratory approaches. However, it generally lacks interpretability. This work demonstrated the feasibility of using Bayesian networks based radiomics for predicting HPV positivity in an interpretable way.

The Value of C-reactive Protein/Albumin Ratio in the Prediction of Contrast-Induced Nephropathy in Emergency Department Patients.

Contrast-induced nephropathy (CIN) is the third most common cause of acute renal failure in hospitalized patients and is an important cause of prolonged hospital stay, morbidity, and mortality. We aimed to investigate the effectiveness and sufficiency of the prognostic capacity of the inflammatory biomarkers C-reactive Protein (CRP) and albumin ratio (CAR) in predicting the development of CIN in patients undergoing contrast-enhanced computed tomography (CT) imaging in the emergency department (ED). This study was performed on patients whose laboratory values ​​could be reached within 48 hours after contrast-enhanced CT imaging in the emergency department of our hospital. The patients were divided into two groups as those with and without CIN according to their increased creatinine levels. Its effectiveness in detecting the development of CIN in the early period was evaluated comparatively. One hundred and twenty-five patients were included. CIN developed in 10.4% of the patients. The CAR was 0.19 (IQR: 0.17-0.33) in the group with CIN and 0.02 (IQR: 0.01-0.06) in the group without CIN; and the difference between the two groups was significant (p<0.001). In multivariate logistic regression analysis, it was found that the CAR increased as an independent risk factor for CIN (OR: 2.326; 95% CI: 1.39-3.893; p=0.001). We think that early identification of patients who may develop CIN through the CAR in EDs and early initiation of treatment for CIN may affect the morbidity-mortality rate and reduce the duration of hospitalization and treatment costs.

Clinics in diagnostic imaging (216).

Clinics in diagnostic imaging (216).

Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry

BackgroundTo develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma based on contrast-enhanced computed tomography (CT).MethodsThis retrospective study included 170 children with blunt hepatic trauma between May 1, 2015, and August 30, 2021, who had undergone contrast-enhanced CT. Both liver parenchyma and liver trauma regions were manually segmented from CT images. Two deep convolutional neural networks (CNNs) were trained on 118 cases between May 1, 2015, and December 31, 2019, for liver segmentation and liver trauma segmentation. Liver volume and trauma volume were automatically calculated based on the segmentation results, and the liver parenchymal disruption index (LPDI) was computed as the ratio of liver trauma volume to liver volume. The segmentation performance was tested on 52 cases between January 1, 2020, and August 30, 2021. Correlation analysis among the LPDI, trauma volume, and the American Association for the Surgery of Trauma (AAST) liver injury grade was performed using the Spearman rank correlation. The performance of severity assessment of pediatric blunt hepatic trauma based on the LPDI and trauma volume was evaluated using receiver operating characteristic (ROC) analysis.ResultsThe Dice, precision, and recall of the developed deep learning framework were 94.75, 94.11, and 95.46% in segmenting the liver and 72.91, 72.40, and 76.80% in segmenting the trauma regions. The LPDI and trauma volume were significantly correlated with AAST grade (rho = 0.823 and rho = 0.831, respectively; p < 0.001 for both). The area under the ROC curve (AUC) values for the LPDI and trauma volume to distinguish between high-grade and low-grade pediatric blunt hepatic trauma were 0.942 (95% CI, 0.882–1.000) and 0.952 (95% CI, 0.895–1.000), respectively.ConclusionsThe developed end-to-end deep learning method is able to automatically and accurately segment the liver and trauma regions from contrast-enhanced CT images. The automated LDPI and liver trauma volume can act as objective and quantitative indexes to supplement the current AAST grading of pediatric blunt hepatic trauma.

Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.

The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures. Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91). All patients underwent nonenhanced, arterial contrast-enhanced, and venous contrast-enhanced CT analysis. The least absolute shrinkage and selection operator regression with 10-fold cross-validation was performed for radiomic models building, and multivariate logistic regression analysis was performed for radiological and combined models building. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC of ROC), and the AUCs were compared using the Delong test. Decision curve analysis was used to evaluate the clinical value of each model. Nomogram and calibration curves were plotted for the combined model. The AUCs for radiological model in the training and validation cohorts were 0.756 and 0.733, respectively. For nonenhanced, arterial contrast-enhanced, venous contrast-enhanced CT and 3-phase images combined radiomics models, the AUCs were 0.940, 0.946, 0.960, and 0.986, respectively, in the training cohort, whereas 0.859, 0.876, 0.930, and 0.923, respectively, in the validation cohort. The combined model, including CT morphology and radiomics signature, showed AUCs of 0.990 and 0.943 in the training and validation cohorts, respectively. Delong test and decision curve analysis showed that the predictive performance and clinical value of the 4 radiomics models and combined model were greater than the radiological model ( P < 0.05). The combined model, including CT morphology and radiomics signature, greatly improved the predictive performance for distinguishing HTET from LTET. Radiomics texture analysis can be used as a noninvasive method for preoperative prediction of the pathological subtypes of TET.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication. To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets. In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma. Contrast-enhanced CT-based radiomics. Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve. In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort. In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors.

Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors. One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model. The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p<0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model. The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

Value of Old Imaging for Patients Undergoing Parathyroidectomy for Primary Hyperparathyroidism

IntroductionAlthough imaging plays no role in diagnosing primary hyperparathyroidism (PHPT), preoperative localization is essential for a focused parathyroidectomy. We hypothesized that reviewing imaging obtained prior to PHPT diagnosis can identify enlarged parathyroid glands and provide information that might potentially impact the preoperative evaluation and intraoperative course of patients undergoing parathyroidectomy. MethodsWe included adult patients with PHPT who underwent parathyroidectomy between October 2015 and October 2020 and had contrast-enhanced computed tomography (CT) imaging of the lower neck and upper chest obtained prior to diagnosis for unrelated indications. A radiologist reviewed the prediagnosis CTs blinded to subsequent parathyroid localization imaging and operative findings. A surgeon assessed the radiologist's findings in the context of each case to determine the potential impact of information from old imaging on surgical decision-making. ResultsWe identified at least one enlarged parathyroid gland on prior contrast-enhanced CT in 30 (75%) of 40 included patients. Despite old imaging enabling correct localization, 60% of these 30 underwent dedicated parathyroid imaging prior to parathyroidectomy. Knowledge of the enlarged parathyroid(s) on prior imaging might have allowed a more focused approach in 10.0% and prompted a more thorough exploration in 13.3%. In the total cohort, reviewing prior imaging could have provided information capable of changing the preoperative evaluation in 52.5% and the operative course in 17.5%. ConclusionsThe identification of enlarged parathyroid glands on contrast-enhanced CT imaging that predates a diagnosis of PHPT is possible. Prospective studies might verify the impact of these findings on the preoperative evaluation and operative course of patients undergoing parathyroidectomy.

Incidence of Internal Carotid Artery Stenosis in Oral Squamous Cell Carcinoma Patients After Neck Dissection.

To determine the incidence of progressive internal carotid artery stenosis (ICAS) by head and neck contrast-enhanced computed tomography (CT) in 45 patients who underwent neck dissection for oral squamous cell carcinoma (OSCC). The study included 45 patients who underwent head and neck contrast-enhanced CT before and after surgery for OSCC by the Hu Yongjie team at the Department of Oral and Maxillofacial-Head & Neck Oncology of Shanghai Ninth People's Hospital in 2016 and were followed up for 5 years. Comparison of the current CT with previously obtained head and neck contrast-enhanced CT images revealed progressive ICAS in 3 patients with a mean age of 50.0 years. All 3 patients were male, and their OSCC sites were the tongue in 2 patients and the buccal in 1 patient. Tumor resection and neck dissection were performed for all 3 patients. Two patients underwent radiotherapy. In all 3 patients, the ICAS had occurred on the same side as the tumors. The results of this study suggest that neck dissection with cervical sheath removal might increase the incidence of ICAS, but this result may need the support of a larger sample size study.

Prognostic predictors of non-small cell lung cancer treated with curative resection: the role of preoperative CT texture features, clinical features, and laboratory parameters

To explore the value of preoperative contrast-enhanced computed tomography (CT) tumour texture characteristics, and clinical and laboratory parameters on the prognosis of curative resection for non-small-cell lung cancer (NSCLC). This retrospective study included 64 patients (34 men and 30 women) with NSCLC who underwent curative resection and were then followed up for 5 years or until death. Preoperative contrast-enhanced CT images, clinical features, and laboratory parameters were collected for these patients. CT texture features of the primary tumour before surgery were extracted from the contrast-enhanced CT images using ImageJ software. Based on the cut-off values determined by X-tile software, the preoperative CT texture features, clinical features, and laboratory parameters were divided into two groups. Kaplan-Meier survival curves and log-rank tests were used to compare the 5-year overall survival (OS) of patients. Multivariate Cox regression analysis was used to determine the independent factors influencing the prognosis. The mean survival was 51.5 months. Tumour volume, entropy, platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and albumin-to-globulin ratio (AGR) were shown to be significantly associated with 5-year OS (p<0.05). Multivariate Cox regression analysis revealed that entropy was the independent factor of prognosis (hazard ratio 4.375, 95% confidence interval [CI]: 1.646-11.620, p=0.003). Entropy is an important and potentially non-invasive imaging biomarker for predicting the prognosis of NSCLC undergoing curative resection.

Severity of traumatic adrenal injury does not meaningfully affect clinical outcomes.

There are limited data comparing the severity of traumatic adrenal injury (TAI) and the need for interventions, such as transfusions, hospitalization, or incidence of adrenal insufficiency (AI) and other clinical outcomes. The aim of this study was to analyze the relationship between the grade of TAI and the need for subsequent intervention and clinical outcomes following the injury. After obtaining Institutional Review Board approval, our trauma registry was queried for patients with TAI between 2009 and 2017. Contrast-enhanced computed tomography (CT) examinations of the abdomen and pelvis were evaluated by a board-certified radiologist with subspecialty expertise in abdominal and trauma imaging, and adrenal injuries were classified as either low grade (American Association for the Surgery of Trauma (AAST) grade I-III) or high grade (AAST grade IV-V). Patients without initial contrast-enhanced CT imaging and those with indeterminate imaging findings on initial CT were excluded. A total of 129 patients with 149 TAI were included. Eight-six patients demonstrated low-grade injuries and 43 high grade. Age, gender, and Injury Severity Score (ISS) were not statistically different between the groups. There was an increased number of major vascular injuries in the low-grade vs. high-grade group (23% vs. 5%, p < 0.01). No patient required transfusions or laparotomy for control of adrenal hemorrhage. There was no statistical difference in hospital length of stay (LOS), ventilator days, or mortality. Low-grade adrenal injuries were, however, associated with shorter ICU LOS (10days vs. 16days, p = 0.03). The need for interventions and clinical outcomes between the low-grade and high-grade groups was similar. These results suggest that, regardless of the TAI grade, treatment should be based on a holistic clinical assessment and less focused on specific interventions directed at addressing the adrenal injury.

Differentiating Central Lung Tumors from Atelectasis with Contrast-Enhanced CT-Based Radiomics Features.

Objectives To evaluate the utility of radiomics features in differentiating central lung cancers and atelectasis on contrast-enhanced computed tomography (CT) images. This study is retrospective. Materials and Methods In this study, 36 patients with central pulmonary cancer and atelectasis between July 2013 and June 2018 were identified. A total of 1,653 2D and 2,327 3D radiomics features were extracted from segmented lung cancers and atelectasis on contrast-enhanced CT. The refined features were investigated for usefulness in classifying lung cancer and atelectasis according to the information gain, and 10 models were trained based on these features. The classification model is trained and tested at the region level and pixel level, respectively. Results Among all the extracted features, 334 2D features and 1,507 3D features had an information gain (IG) greater than 0.1. The highest accuracy (AC) of the region classifiers was 0.9375. The best Dice score, Hausdorff distance, and voxel AC were 0.2076, 45.28, and 0.8675, respectively. Conclusions Radiomics features derived from contrast-enhanced CT images can differentiate lung cancers and atelectasis at the regional and voxel levels.

Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation.

BackgroundMutation screening for gastrointestinal stromal tumor (GIST) is crucial and the c kit gene (KIT) exon 11 mutation is the most common type. This study aimed to explore the associations between GIST with KIT exon 11 mutation and contrast-enhanced computed tomography (CT) images.MethodsPathologically proven GISTs with definitive genotype testing results in our hospital were retrospectively included. Abdominal contrast-enhanced CT images were analyzed. Conventional CT image features and radiomic features were recorded and extracted to build the following models: model [CT], model [radiomic + clinic] and model [CT + radiomic + clinic]. The diagnostic performances of GISTs with KIT exon 11 mutation and KIT exon 11 deletion involving codons 557–558 were evaluated.ResultsIn total, 327 GISTs (255 with KIT exon 11 mutation, and 73 with KIT exon 11 mutation deletion involving codons 557–558) were included. Significant CT features were found for GISTs with KIT exon 11 mutation. The area under curves (AUCs) of the models for KIT exon 11 mutation were 0.7158, 0.7530, and 0.8375 in the training cohort, and 0.6777, 0.7349, and 0.8105 in validation cohort, respectively. The AUCs of the models for KIT exon 11 mutation deletion involving codons 557–558 were 0.7155, 8621, and 0.8691 in the training cohort, and 0.7099, 0.8355, and 0.8488 in the validation cohort, respectively. The model [CT + radiomic + clinic] demonstrated the highest AUCs for prediction of KIT exon 11 mutation and those with deletion involving codons 557–558 (P<0.05), respectively. The model [radiomic + clinic] showed higher diagnostic performance than model [CT] significantly.ConclusionsOur results demonstrated the associations between GIST with KIT exon 11 mutation and contrast-enhanced CT images. We found combing conventional image analysis and texture analysis is a useful tool to distinguish GIST with KIT exon 11 mutation. CT radiogenomics exhibited good application potential in predict the KIT exon 11 mutation of GIST.

Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non–muscle-invasive bladder cancer with CT

ObjectiveTo construct a deep-learning convolution neural network (DL-CNN) system for the differentiation of muscle-invasive bladder cancer (MIBC) and non–muscle-invasive bladder cancer (NMIBC) on contrast-enhanced computed tomography (CT) images in patients with bladder cancer. Materials and methodsA total of 1200 cross-sectional CT images were obtained from 369 patients with bladder cancer receiving radical cystectomy from January 2015 to June 2018, including 249 non–muscle-invasive bladder cancer (NMIBC) series and 120 muscle-invasive bladder cancer (MIBC) series. All eligible images were distributed randomly into the training, validation, and testing cohorts with ratios of 70 %, 15 %, and 15 %, respectively. We developed one small DL-CNN containing four convolutional and max pooling layers and eight DL-CNNs with pretrained bases from the ImageNet dataset to differentiate NMIBC from MIBC. The intermediate activations were applied on the test dataset to visualize how successive DL-CNN layers transform their input. ResultsThe area under the receiver operating characteristic curve (AUROC) of the validation and testing datasets for the small DL-CNN was 0.946 and 0.998, respectively. The AUROCs of eight deep learning algorithms with pretrained bases ranged from 0.762 to 0.997 in the testing dataset. The VGG16 model had the largest AUROC of 0.997 among the eight algorithms with a sensitivity and specificity of 0.889 and 0.989. The independent features encoded by the small DL-CNN filters were displayed as assemblies of individual channels. ConclusionBased on contrast-enhanced CT images, our DL-CNN system could successfully classify NMIBC and MIBC with favorable AUROC in patients with bladder cancer. The application of our system in early stage might assist the pathological examination for the improvement of diagnostic accuracy.

Morphometric analysis of the splenic artery using contrast-enhanced computed tomography (CT)

PurposeTo evaluate the morphology and course of the splenic artery, which might impact the surgical implantation of systems that stimulate the nerves surrounding the splenic artery. Experimental studies indicate that these nerves play an important part in immune modulation, and might be a potential target in the treatment of autoimmune diseases.MethodsThis retrospective cohort study made use of contrast-enhanced CT images from 40 male and 40 female patients (age 30–69) that underwent a CT examination of the aorta, kidneys or pancreas. Anatomic features were described including total splenic artery length, calibers, tortuosity, the presence of arterial loops and the branching pattern of the splenic artery.ResultsNo age-gender-related differences could be found related to tortuosity or branching pattern. The length of splenic artery in contact with pancreatic tissue decreased with increasing age, but was not different between genders. Artery diameters were wider in male compared to female subjects. Loops of variable directions, that represent a part of the artery that curls out of the pancreatic tissue, were identified in each age-gender category and were present in nearly all subjects (86%).ConclusionThis study suggests that although some anatomic features of the splenic artery are subject to factors as age and gender, the tortuosity of the splenic artery is not age dependent. Most subjects had one or multiple loops, which can serve as a target for neuromodulatory devices. Future studies should investigate whether splenic nerve stimulation is safe and feasible.

A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection

Aortic dissection (AD) is one of the most common aortic diseases, where blood enters the aortic wall through the aortic intimal rift and causes separation of the arterial wall. Any delay or misdiagnosis can have severe consequences for patients with aortic dissection and even lead to higher mortality rates. Therefore, rapid and accurate detection of aortic dissection saves patients valuable time and provides assistance for the selection of clinical treatment options. This paper describes a deep learning algorithm that uses contrast-enhanced CT images for segmentation and automatic detection of aortic dissection. First, we construct a U-Net based semantic segmentation architecture and apply it to contrast-enhanced CT images to segment the aortic true lumen. Then, we use the segmentation results for aortic circularity analysis to obtain slice-level detection results. Finally, we aggregated the slice-level results to present patient-level detection results. We tested our algorithm on 20 contrast-enhanced CT datasets, of which 10 were aortic dissections. In terms of temporal performance, we have achieved millisecond prediction on sliced images. At the same time, we achieved 85.00% accuracy, 90.00% sensitivity and 80.00% specificity in patient-level testing.

Anatomic Eligibility for Commercial Branched Endograft Repair of Thoracoabdominal Aortic Aneurysms

First-generation "off-the-shelf" branched endovascular stent grafts are in development for treatment of thoracoabdominal aortic aneurysms (TAAAs). Prior studies have assessed eligibility rates among highly selected cohorts of patients referred for endovascular treatment, and the broader applicability of these devices to all patients with TAAA is unknown. The aims of this study were to assess the overall suitability of the 3 commercial 4-branched devices with or without adjunct procedure(s) in an unselected cohort of patients with TAAA and to identify areas for improvement in the next generation of devices. A retrospective review of three-dimensional centerline reconstructions of contrast-enhanced computed tomography (CT) imaging was performed in consecutive patients with TAAA seen between 2013 and 2017. All patients with contrast-enhanced CT imaging were included, regardless of prior evaluation for suitability for endovascular repair. Eligibility for a device was assessed based on instructions for use (IFU) from the device manufacturer along with prespecified anatomic criteria. Adjunct procedures were defined as carotid-subclavian revascularization, target vessel endovascular intervention, and iliac conduit/revascularization. Of 165 patients with TAAA, 122 had CT scans adequate for study inclusion. Eighteen patients (14.8%) were eligible for at least 1 device by IFU, and 41 (33.6%) could have been made eligible for at least 1 device by an adjunct procedure. Sixty-three (51.6%) were not eligible for any device within IFU even with adjunct procedures, including 31 of 32 patients with TAAA due to dissection. The most common reasons for ineligibility were perivisceral flow channel diameter <20mm (n=43) and an inadequate proximal seal zone (n=29). Women were significantly less likely to be eligible for an off-the-shelf device (P=0.03) and were more likely to require an iliac procedure to become eligible (P=0.006). Almost none of the patients with dissection could receive a device even if adjunct procedures were used. Over half of patients with TAAA could not be made eligible for an off-the-shelf device based on manufacturers' criteria, even with adjunct procedures. Women and patients with TAAA due to dissection had higher rates of ineligibility. These data demonstrate that custom fenestrated devices and low-profile devices are needed to expand eligibility for endovascular repair of TAAA.

Aberrant gastric venous drainage and associated atrophy of hepatic segment II: computed tomography analysis of 2021 patients.

To investigate the prevalence and patterns of aberrant gastric venous drainage and associated atrophy of the hepatic segment on contrast-enhanced computed tomography (CT). Two radiologists retrospectively reviewed contrast-enhanced CT images from 2021 patients who underwent cone-beam CT-guided chemoembolization between January 2013 and December 2018. They determined the presence or absence of an aberrant gastric vein(s) and its drainage site by consensus, and qualitatively analyzed the presence or absence of atrophy of segments II or III. In cases of disagreement between the two reviewers regarding the presence of atrophy, quantitative analysis was performed using dedicated software. A total of 31 aberrant right gastric veins were revealed on CT (1.5%), most of which drained into P2 (n = 8), the S2/3 border zone (n = 8), S2 (n = 6), or S4 (n = 5). An aberrant left gastric vein was observed in 21 (1.0%) patients, and P2 was the most common drainage site (n = 13) in these patients. Atrophy of segment II was more frequently observed among patients with aberrant gastric veins than among those without (26.9% versus 4.1%; p < 0.001). In addition, an aberrant gastric vein draining into P2 was most frequently accompanied by segment II atrophy (47.6%). Aberrant right and left gastric veins were observed in 1.5% and 1.0% of patients, respectively. Atrophy of segment II was frequently observed in patients with aberrant gastric veins, especially those that drained into P2.