Contrast-enhanced Computed Tomography Data Research Articles

Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.

Personalising management of primary oesophageal adenocarcinoma requires better risk stratification. Lack of independent validation of proposed imaging biomarkers has hampered clinical translation. We aimed to prospectively validate previously identified prognostic grey-level co-occurrence matrix (GLCM) CT features for 3-year overall survival. Following ethical approval, clinical and contrast-enhanced CT data were acquired from participants from five institutions. Data from three institutions were used for training and two for testing. Survival classifiers were modelled on prespecified variables ('Clinical' model: age, clinical T-stage, clinical N-stage; 'ClinVol' model: clinical features + CT tumour volume; 'ClinRad' model: ClinVol features + GLCM_Correlation and GLCM_Contrast). To reflect current clinical practice, baseline stage was also modelled as a univariate predictor ('Stage'). Discrimination was assessed by area under the receiver operating curve (AUC) analysis; calibration by Brier scores; and clinical relevance by thresholding risk scores to achieve 90% sensitivity for 3-year mortality. A total of 162 participants were included (144 male; median 67years [IQR 59, 72]; training, 95 participants; testing, 67 participants). Median survival was 998days [IQR 486, 1594]. The ClinRad model yielded the greatest test discrimination (AUC, 0.68 [95% CI 0.54, 0.81]) that outperformed Stage (ΔAUC, 0.12 [95% CI 0.01, 0.23]; p = .04). The Clinical and ClinVol models yielded comparable test discrimination (AUC, 0.66 [95% CI 0.51, 0.80] vs. 0.65 [95% CI 0.50, 0.79]; p > .05). Test sensitivity of 90% was achieved by ClinRad and Stage models only. Compared to Stage, multivariable models of prespecified clinical and radiomic variables yielded improved prediction of 3-year overall survival. Previously identified radiomic features are prognostic but may not substantially improve risk stratification on their own. • Better risk stratification is needed in primary oesophageal cancer to personalise management. • Previously identified CT features-GLCM_Correlation and GLCM_Contrast-contain incremental prognostic information to age and clinical stage. • Compared to staging, multivariable clinicoradiomic models improve discrimination of 3-year overall survival.

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

BackgroundThere is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen.MethodsWe conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong’s method.ResultsAll six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979.ConclusionsThe machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

Pediatric adrenocortical carcinoma: clinical features and application of neoadjuvant chemotherapy

ObjectiveTo summarize the clinical characteristics of children with adrenocortical carcinoma (ACC) and preliminarily explore the indications for and efficacy of neoadjuvant chemotherapy in certain patients.MethodsThe data of 49 children with adrenocortical tumors (ACT) in the past 15 years were retrospectively analyzed, and after pathology assessment using Weiss system grading, 40 children diagnosed with ACC were included. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and three-dimensional (3D) reconstruction of contrast-enhanced computed tomography data were used to evaluate the response to neoadjuvant chemotherapy.ResultsForty patients (17 males, 23 females) with ACC were enrolled. Abnormal hormone levels were common in children with ACC (n = 31), and in terms of clinical presentation, sexual precocity was the most common (n = 14, 35.0%), followed by Cushing’s syndrome (n = 12, 30.0%). Seven of 40 children received neoadjuvant chemotherapy due to a maximum lesion diameter greater than 10 cm (n = 4), invasion of surrounding tissues (n = 2), intravenous tumor thrombus (n = 2), and/or distant metastasis (n = 2); 2 patients achieved partial response, and 5 had stable disease according to the RECIST 1.1 standard. Furthermore, 3D tumor volume reconstruction was performed in 5 children before and after neoadjuvant chemotherapy. Tumor volumes were significantly reduced in all 5 children, with a median volume reduction of 270 (interquartile range, IQR 83, 293) (range: 49–413) ml. After surgery with/without chemotherapy, the 5-year overall survival rate for all children was 90.0% (95% CI-confidence interval 80.0–100.0%), and the 5-year event-free survival rate was 81.5% (95% CI 68.0–97.7%).ConclusionIn the diagnosis and treatment of pediatric ACC, a comprehensive endocrine evaluation is necessary to facilitate early diagnosis. Surgery and chemotherapy are important components of ACC treatment, and neoadjuvant chemotherapy should be considered for children with ACC who meet certain criteria, such as a large tumor, distant metastases, or poor general condition.

Impact of body weight and sex in selected dog breeds on the canine adrenal gland dimensions measured by computed tomographic imaging

BackgroundThe present study aimed to investigate possible influences of body weight and sex on adrenal gland size in endocrinologically healthy dogs. Possible factors influencing the adrenal size are discussed in relation to a universal upper reference value from the literature of 7.4 mm as the thickness in the caudal pole of the adrenal gland. The adrenal size was measured by computed tomography (CT) from 66 normal dogs of six different breeds (Labrador Retriever (n = 16), German Shepherd (n = 10), Boxer (n = 8), Beagle (n = 14), Dachshund (n = 6) and Jack Russell Terrier (n = 12); male n = 38 (thereof neutered n = 23), female n = 28 (thereof neutered n = 17)) based on volume quantification and linear measurements using the data processing software Amira. For interbreed comparability, a ratio consisting of the third root of adrenal volume to aortic diameter (Ratio volume-aorta, RVA) was introduced. Additionally, breed-related attenuation values in contrast-enhanced CT data sets were measured.ResultsThe measured volumes ranged from 0.34 to 1.93 cm3 for the right and from 0.39 to 2.23 cm3 for the left adrenal gland. The present study was able to demonstrate a body weight effect on the adrenal volume as well as on length and height. In terms of adrenal size, no significant differences between male and female, nor between intact and neutered dogs were obtained due to the RVA. In addition, for the weight classes, a breed independent threshold for dogs less (left 1.4; right gland 1.5) or more than 20 kg body weight (left 1.1; right gland 1.2) based on RVA was defined. Breed-related significant differences with respect to attenuation were determined only for the left adrenal gland, with lower attenuation values in large dog breeds.ConclusionThe present study points out the importance of weight-related data when assessing CT data of the canine adrenal gland regarding volume, size and attenuation. The use of a universal reference value for the assessment of adrenal size appears unsuitable considering weight-related volume and linear measurements. Sex seems not to affect adrenal gland size.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Dynamic contrast-enhanced CT and clinical features of sarcomatoid hepatocellular carcinoma

PurposeTo investigate the dynamic contrast-enhanced computed tomography (CECT) features and clinical characteristics of sarcomatoid hepatocellular carcinoma (S-HCC).MethodsWe retrospectively reviewed the CECT data and clinical findings of 13 patients (11 male and 2 female, with an average age of 58.6 ± 11.2 years) with pathologically proven S-HCC, including 9 patients with surgical resection and 4 patients with biopsy examination. All patients underwent CECT scans. Two radiologists reviewed and evaluated general features, CECT features and extratumoral features of each lesions based on a consensus.ResultsAmong the thirteen tumors, a mean size of 66.7 mm was observed, ranging in diameter from 30 to 146 mm. Seven of thirteen patients had hepatitis B virus (HBV) infection and an elevation of alpha-fetoprotein (AFP) level. Most of cases located in the right lobe of liver (84.6%, 11/13). Nine of thirteen tumors showed lobulated or wavy contours and infiltrative morphology, while eight tumors presented with unclear margin. The tumor textures were mainly heterogeneous for ischemia or necrosis, with solid components dominantly in all cases. Eight of thirteen tumors exhibited “slow-in and and slow-out” dynamic enhancement pattern in CECT, with a enhancement peak in the portal venous phase. Portal vein or hepatic thrombus, adjacent organs invasion and lymph node metastasis were observed in two patients, respectively. Four of thirteen lesions occurred intrahepatic metastasis and hepatic surface retraction respectively.ConclusionS-HCC gengerally seen in elderly male with HBV infection and elevated AFP level. The CT manifestations including: large diameter, frequently hepatic right lobe involvement, lobular or wavy contours, ill-defined margins, infiltrative morphology, obvious heterogeneity and dynamic enhancement pattern of “slow-in and and slow-out” , contributed to the diagnosis of S-HCC. These tumors usually occurred hepatic surface retraction and intrahepatic metastasis.

Prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma based on preoperative contrast-enhanced CT and clinical data

ObjectiveMicrovascular invasion (MVI) is significantly associated with prognosis in combined hepatocellular-cholangiocarcinoma (cHCC-CCA) patients. The study aimed to explore the value of preoperative contrast-enhanced CT (CECT) features and clinical data in predicting MVI of cHCC-CCA. MethodsA total of 33 patients with MVI-positive and 27 with MVI-negative were enrolled, and underwent preoperative CECT imaging from January 2016 to December 2021. Preoperative clinical data and CECT imaging features were retrospectively analyzed. Univariable and multivariable logistic regression analysis were performed to identify potential predictors of MVI in cHCC-CCA. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC) value. ResultsThe mean age of the patients was 54.0 ± 10.3 years, and 53 of the 60 patients (88.3%) were male. Preoperative imaging features on CECT (non-smooth contour and arterial phase peritumoral enhancement) and clinical data (hepatitis B virus (HBV) infection and protein induced by vitamin K absence or antagonist-II (PIVKA-II)) were highly distinct between those in MVI-positive group and MVI-negative group. On multivariable logistic analysis, arterial phase peritumoral enhancement (odds ratio (OR), 6.514; 95% confidence interval (CI), 1.588–26.728, p = 0.012) and high serum PIVKA-II level (OR, 6.810; 95% CI, 1.796–25.820, p = 0.005) were independent predictors associated with MVI of cHCC-CCA. The combination of these two predictors had high sensitivity (31/33, 93.9%; 95% CI, 80.4% − 98.3%) in the prediction of MVI with an area under the receiver operating characteristic (ROC) curve of 0.763 (95% CI, 0.635–0.863). ConclusionsThe findings indicated that arterial phase peritumoral enhancement on preoperative CECT and high serum PIVKA-II level were identified as potential predictors for MVI in cHCC-CCA patients.

The renal artery-aorta angle associated with renal artery plaque: a retrospective analysis based on CT

PurposeTo investigate the relationship between renal artery anatomical configuration and renal artery plaque (RAP) based on 320-row CT.MethodsThe abdominal contrast-enhanced CT data from 210 patients was retrospectively analyzed. Among 210 patients, there were 118 patients with RAP and 92 patients with no RAP. The anatomical parameters between lesion group and control group were compared and analyzed by using t-test, χ2-test and logistic regression analysis.Results(1) There were statistical differences on age, hypertension, diabetes, hypertriglyceridemia and hypercholesterolemia between lesion group and control group. (2) The differences on the distribution and type and of RAP between lesion group and control group were statistically significant. The most common position was the proximal, and the most common type was calcified plaque. (3)There were significant statistical differences on the proximal diameter of renal artery and renal artery-aorta angle A between lesion group and control group. The differences on the other anatomical factors between two groups were not statistically significant. (4) The result of logistic regression analysis showed that right RAP was related to age, hypertension and right renal artery angle A (the AUC of ROC = 0.82), and left RAP was related to high serum cholesterol, age and left renal artery angle A(the AUC of ROC = 0.83). (5) The RAP was associated with renal artery-aorta angle A, but the differences on distribution, type stability of RAP between R1 (L1) group and R2 (L2) group were not statistically significant.ConclusionsThe RAP was associated with age, hypertension, hypercholesterolemia and renal artery-aorta angle A. Adults which had the greater renal artery-aorta angle A and the other above risk factors may be at increased risk for RAP.

NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation

Background and objectivesNon-contrast CT (NCCT) and contrast-enhanced CT (CECT) are important diagnostic tools with distinct features and applications for chest diseases. We developed two synthesizers for the mutual synthesis of NCCT and CECT and evaluated their applications. MethodsTwo synthesizers (S1 and S2) were proposed based on a generative adversarial network. S1 generated synthetic CECT (SynCECT) from NCCT and S2 generated synthetic NCCT (SynNCCT) from CECT. A new training procedure for synthesizers was proposed. Initially, the synthesizers were pretrained using self-supervised learning (SSL) and dual-energy CT (DECT) and then fine-tuned using the registered NCCT and CECT images. Pulmonary vessel segmentation from NCCT was used as an example to demonstrate the effectiveness of the synthesizers. Two strategies (ST1 and ST2) were proposed for pulmonary vessel segmentation. In ST1, CECT images were used to train a segmentation model (Model-CECT), NCCT images were converted to SynCECT through S1, and SynCECT was input to Model-CECT for testing. In ST2, CECT data were converted to SynNCCT through S2. SynNCCT and CECT-based annotations were used to train an additional model (Model-NCCT), and NCCT was input to Model-NCCT for testing. Three datasets, D1 (40 paired CTs), D2 (14 NCCTs and 14 CECTs), and D3 (49 paired DECTs), were used to evaluate the synthesizers and strategies. ResultsFor S1, the mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were 14.60± 2.19, 1644± 890, 34.34± 1.91, and 0.94± 0.02, respectively. For S2, they were 12.52± 2.59, 1460± 922, 35.08± 2.35, and 0.95± 0.02, respectively. Our synthesizers outperformed the counterparts of CycleGAN, Pix2Pix, and Pix2PixHD. The results of ablation studies on SSL pretraining, DECT pretraining, and fine-tuning showed that performance worsened (for example, for S1, MAE increased to 16.53± 3.10, 17.98± 3.10, and 20.57± 3.75, respectively). Model-NCCT and Model-CECT achieved dice similarity coefficients (DSC) of 0.77 and 0.86 on D1 and 0.77 and 0.72 on D2, respectively. ConclusionsThe proposed synthesizers realized mutual and high-quality synthesis between NCCT and CECT images; the training procedures, including SSL pretraining, DECT pretraining, and fine-tuning, were critical to their effectiveness. The results demonstrated the usefulness of synthesizers for pulmonary vessel segmentation from NCCT images.

Differentiating Peripherally Located Pulmonary Noncalcified Hamartoma From Carcinoid Using CT Radiomics Approaches.

This article aimed to differentiate noncalcified hamartoma from pulmonary carcinoid preoperatively using computed tomography (CT) radiomics approaches. The unenhanced CT (UECT) and contrast-enhanced CT (CECT) data of noncalcified hamartoma (n = 73) and pulmonary carcinoid (n = 54; typical/atypical carcinoid = 13/41) were retrospectively analyzed. The patients were randomly divided into the training and validation sets. A total of 396 radiomics features were extracted from UECT and CECT, respectively. The features were selected by using the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator to construct a radiomics model. Clinical factors and radiomics features were integrated to build a nomogram model. The performance of clinical factors, radiomics, and nomogram models on the differential diagnosis between noncalcified hamartoma and carcinoid were investigated. Diagnostic performance of radiologists was also explored. In regard to distinguishing noncalcified hamartoma from carcinoid, the areas under the receiver operating characteristic curves of the clinical, radiomics, and nomogram models were 0.88, 0.94, and 0.96 in the training set UECT, and were 0.85, 0.92, and 0.96 in the training set CECT, respectively. The areas under the curve of the 3 models were 0.89, 0.96, and 0.96 in the validation set UECT, and were 0.79, 0.90, and 0.94 in the validation set CECT, respectively. The nomogram model exhibited good calibration and was clinically useful by decision curve analysis. Nomogram did not show significant improvement compared with radiomics, neither for UECT nor for CECT. Diagnostic performance of radiologists was lower than both radiomics and nomogram model. Radiomics approaches may be useful in distinguishing peripheral pulmonary noncalcified hamartoma from carcinoid. Radiomics features extracted from CECT provided no significant benefit when compared with UECT.

Segmental volumetric analysis with a ventilated or perfused area: identifying the intersegmental plane.

Two methods are available to identify the intersegmental plane during segmentectomy: the inflation-deflation method, based on the ventilation area, and injection of indocyanine green, based on the pulmonary arterial distribution. However, whether the intersegmental plane created by these 2 methods matches remains unknown. Our goal was to identify the demarcation lines based on bronchial and arterial territories using 3-dimensional computed tomography-based volumetry. We collected data from patients who underwent thoracoscopic segmentectomy in our hospital between April 2012 and May 2021. Three-dimensional images were reconstructed from the preoperative contrast-enhanced computed tomography data using the SYNAPSE VINCENT software program. The volume of the affected area and the distance of the tumour from the intersegmental plane were calculated based on each affected artery and bronchus. Each calculated volume was compared to each affected segment using a paired t-test. Of 195 patients, 114 underwent upper lobe segmentectomy, and 81 underwent lower lobe segmentectomy. In upper lobe segmentectomy, the affected arterial segmental volume was smaller than the bronchial volume (505.0 ml vs 539.4 ml, P < 0.001). In lower lobe segmentectomy, there was no significant difference between arterial and bronchial volumes (234.6 ml vs 236.9 ml, P = 0.607). The volume of the affected arterial segmental lung and the distance of the tumour from the intersegmental plane were significantly smaller than the bronchial volume in upper lobe segmentectomies. As per the results, the affected segmental volume delineated by the indocyanine green method would be underestimated in upper lobe segmentectomy.

Anemia Detection by Hemoglobin Quantification on Contrast-enhanced Photon-counting CT Data Sets.

See also the editorial by Dodd and MacDermott in this issue.

Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors.

Objectives This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.

Extrahepatic Disease in Hepatocellular Carcinoma: Do We Always Need Whole-Body CT or Is Liver MRI Sufficient? A Subanalysis of the SORAMIC Trial.

Background: To investigate whole-body contrast-enhanced CT and hepatobiliary contrast liver MRI for the detection of extrahepatic disease (EHD) in hepatocellular carcinoma (HCC) and to quantify the impact of EHD on therapy decision. Methods: In this post-hoc analysis of the prospective phase II open-label, multicenter, randomized controlled SORAMIC trial, two blinded readers independently analyzed the whole-body contrast-enhanced CT and gadoxetic acid-enhanced liver MRI data sets of 538 HCC patients. EHD (defined as tumor manifestation outside the liver) detection rates of the two imaging modalities were compared using multiparametric statistical tests. In addition, the most appropriate treatment recommendation was determined by a truth panel. Results: EHD was detected significantly more frequently in patients with portal vein infiltration (21% vs. 10%, p < 0.001), macrovascular infiltration (22% vs. 9%, p < 0.001), and bilobar liver involvement (18% vs. 9%, p = 0.006). Further on, the maximum lesion diameter in patients with EHD was significantly higher (8.2 cm vs. 5.8 cm, p = 0.002). CT detected EHD in significantly more patients compared to MRI in both reader groups (p < 0.001). Higher detection rates of EHD in CT led to a change in management only in one patient since EHD was predominantly present in patients with locally advanced HCC, in whom palliative treatment is the standard of care. Conclusions: Whole-body contrast-enhanced CT shows significantly higher EHD detection rates compared to hepatobiliary contrast liver MRI. However, the higher detection rate did not yield a significant impact on patient management in advanced HCC.

Comparative Growth of the Tympanic Cavity in Primates

Primate auditory structures are morphologically indicative of taxonomy and phylogeny. The tympanic cavity houses the three articulating ear ossicles: malleus, incus, and stapes. The ossicles amplify sound waves from mechanical vibrations to the cochlea, a sensory organ, where it can then be interpreted by the organism. All primates are characterized by a petrous bulla, but the extent of the tympanic cavity varies across taxa.Our sample of newborn and adult individuals representing strepsirrhines (e.g., Galago moholi and Nycticebus pygmaeus) and haplorhines (e.g., Aotus nancymaae and Cebuella pygmaea) allowed us to study the comparative growth of these structures. Using a combination of conventional CT data and diffusion iodine contrast‐enhanced CT (diceCT) data, we created 3D models of auditory structures using 3DSlicer. Metric data included angles between ear ossicles, surface area of the tympanic membrane, surface area of the stapes footplate, and overall tympanic cavity volume. Many differences among major clades are already evident at the newborn stages in this sample. The pneumatization of accessory cavities in newborns has been initiated. For example, in the newborn Galago moholi, pneumatization of accessory cavities is most pronounced posteriorly. In contrast, in the newborn Aotus nancymaae, we see early evidence of the anterior accessory cavity. Mesh‐to‐mesh comparisons of endocasts of the cavity indicate structural changes through ontogeny. Additionally, the surface area of the tympanic membrane remains consistent through growth.This research allows for a better understanding of the relationship between the functional morphology of the auditory system and audible frequency levels. Haplorhines are adapted to lower frequencies than strepsirrhines. This adaptation is discernable at birth, and key spatial relationships of ossicles are maintained through ontogeny even as spatial configuration of the cranium as a whole undergoes profound change due to pneumatic expansion of sinus cavities and growth of soft tissue structures.

Mathematical postprocessing analysis of contrast-enhanced computed tomography data of the kidneys in evaluation of split renal function in patients with kidney stone disease

Introduction. An original research work was performed to assess split kidney function by glomerular filtration rate (GFR) with mathematical analysis of the kidneys computed tomography (CT) data in patients with kidney stone disease (KSD). Objective was to evaluate the GFR and the parenchyma structure of each kidney and identify the possible patterns of contrast medium intrarenal transport with mathematical analysis of the kidneys CT data in patients with KSD.Materials and methods. Data of 27 patients of both genders with KSD were retrospectively analyzed. To evaluate GFR separately for each kidney we analyzed the data of contrast-enhanced CT (GFR reference values are 0.55 % of contrast medium per second). Inclusion criteria are as follows: 1) newly diagnosed SKD; 2) stone size ≤1,5-2,0 cm, no obstruction of the urine flow registered; 3) no kidney or upper urinary tract surgical history; 4) age - ≤45 years; 5) no severe chronic diseases. All of these allowed to minimize influence of any other disorders on split renal function except for SKD and conduct per se research.Results. The mathematic analysis of the contrast-enhanced CT data revealed GFR changes in 26 (96.3 %) out of 27 patients. Hyperfiltration was found in 12 (44.4 %) patients: right kidney GFR - 0.6-0.77 %, mean value - 0.65 %; left kidney GFR - 0.59-0.79 %, mean value - 0.67 %. Hypofiltration was found in 13 (48.1 %) patients: right kidney GFR - 0.2-0.54 %, mean value - 0.37 %; left kidney GFR - 0.2-0.53 %, mean value - 0.4 %. The GFR values significantly differed between the groups both for the right (p = 0.000014) and left (p = 0.000045) kidneys. We found no significant age-related difference between the groups (p = 0.895). As well as that no significant differences in Resistance Index both in magistral (right kidney: p = 0.221; left kidney: p = 0.850) and segmental (right kidney: p = 0.306; left kidney: p = 0.957) arteries between the groups with hyperfiltration and hypofiltration were observed. One patient demonstrated no changes in GFR, and the other one had hyperfiltration (0,62 %) in one kidney and hypofiltration (0,48 %) in another.Conclusion. Most of the patients (92.6 %) with SKD demonstrate GFR changes (either hyperfiltration or hypofiltration) that may indicate the disturbed intrarenal blood and urine flow through the kidney.

Three-dimensional image simulation for lung segmentectomy from unenhanced computed tomography data.

We developed a novel three-dimensional (3D) image simulation system, focused on pulmonary segmentectomy. The novel algorithms run by the software, which are independent of the differences in computed tomography (CT) values of vascular structures, enabled the creation of 3D images from unenhanced CT data with accuracy comparable to that from contrast-enhanced CT data. To evaluate the anatomical accuracy, we compared it between images created from unenhanced and contrast-enhanced CT in seven patients who underwent thoracoscopic segmentectomy. With regard to the automatic recognition of pulmonary vessels, the 3D image from unenhanced CT falsely recognized one or two points in two cases, whereas that from contrast-enhanced CT false recognitions in one case. Both 3D images had similar creation time and capability for identifying the intersegmental plain. The novel 3D image simulation for segmentectomy from unenhanced CT had sufficient anatomical accuracy for practical use but required attention due to inevitable minor false recognition.

Diagnostic benefit of multiparametric MRI over contrast-enhanced CT in patients with bladder cancer: A single-center 1-year experience

PurposeTo assess the clinical applicability of local tumor staging in urinary bladder cancer (BC) with preoperative multiparametric MRI (mpMRI) using the five-point Vesical Imaging-Reporting and Data System (VI-RADS) scoring system and to compare it to dual-phase contrast-enhanced computed tomography (CECT). Methods33 patients with primary untreated bladder cancer underwent CECT followed by preoperative multiparametric 3.0 T MRI between July 2019 and August 2020 and were enrolled in this retrospective study. Two radiologists initially performed staging on the CECT image data sets and - blinded to CT results - on subsequent mpMRI. BCs were staged according to the VI-RADS scoring system. Postoperative pathology was correlated to the VI-RADS score and the CECT results. The performance of VI-RADS in determining detrusor muscle invasion was analyzed using a receiver operating characteristic curve. Based on the histopathology, sensitivity, specificity and accuracy for muscle invasiveness between both image modalities were compared using the Chi square test. ResultsA total of 33 patients (29 male, median age 70 years, IQR: 59–81 years) were included. 10 tumors were categorized as non-muscle invasive (30%) and 23 as muscle invasive BC (70%) in final histology. Tumor stages were correctly assigned as being either muscle invasive or non-muscle invasive on both CECT and mpMRI with regard to both early and late stages of BC (Ta-Tis and T3a-T4b). T-stages bordering the histopathologic limits of muscle invasiveness (T1-T2a-b) resulted in overestimation of muscle invasion in 43% of cases (VI-RADS 3–4) for the mpMRI image data sets and in an underestimation of muscle invasion in up to 55.5% of cases analysing the CECT data. Sensitivity and specificity for the determination of muscle invasion in CECT and mpMRI were 80%/80% and 74%/61% for Radiologist#1 and 70%/90% and 83%/70% for Radiologist#2, respectively. ConclusionsThere are advantages and disadvantages of both CECT and mpMRI when used in the clinical assessment of BC muscular tumor invasion. In borderline cases, only the combination of cross-sectional imaging and histopathological staging may help in making the optimal treatment decisions.

Fully automatic detection and measurement of abdominal aortic aneurysm using artificial intelligence

Abstract Background Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the decision to treat relies on the exact detection and size of AAA, which is time-consuming and prone to high inter-reader variability. Artificial intelligence (AI) has revealed new insights into the management of AAA, but it needs large amounts of contrast-enhanced CT data. Moreover, there has been few studies about the fully automatic detection and measurement of AAA. Purpose The aim of this study is to evaluate the accuracy of AI for the detection and measurement of AAA using small amounts of non-contrast CT. Methods We retrospectively collected 145 non-contrast CT scans of suspected AAA. The first step was to manually segment the 30 non-contrast CT scans to create training data for the AI, and the second step was to improve accuracy using 9 of non-contrast CT scans. Image processing is used to identify the AAA area and automatically measure the size. The AAA region was identified from the amount of change in the minor-axis, and the maximum minor-axis diameter was calculated using elliptical fitting. To evaluate the diagnostic utility of AI for the detection of AAA, the sensitivity and positive predictive value were calculated referred for the diagnostic report. The reproducibility in the size of AAA was assessed using intraclass correlation coefficients (ICCs) between the diagnostic report and AI. Results Among 145 of non-contrast CT scans, 111 had AAA. The sensitivity and positive predictive value of AI for the detection of AAA were 94.6% and 53.8%, respectively (Picture 1). The size of AAA calculated by the AI (42.5±8.8 mm) showed a strong correlation with the those of diagnostic reports (44.6±8.4 mm; ICCs = 0.97) (Picture 2). Conclusions AI represents a useful tool in the fully automatic detection and measurement of AAA using small amounts of non-contrast CT. Funding Acknowledgement Type of funding sources: None. Picture 1Picture 2

Model-free automatic segmentation of the aortic valve in multislice computed tomography images

Valvular diseases may affect one or more of the cardiac valves, which may need to be replaced or restored for effective treatment. The surgical procedure can be guided by a patient-specific and dynamic model containing information complementary to the 2D/3D static images of the valves. To this end, in this study a novel automated model-free aortic valve segmentation method is presented, and its performance is evaluated against expert annotations over conventional contrast-enhanced ECG-gated multislice CT data of the aortic valve at its closed position. Detailed evaluation of the proposed method in 19 real cases revealed an encouraging performance of 3D region growing over Hessian based approach but also demonstrated the complexity of the problem.