Computed Tomography Angiography Images Research Articles

A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study.

Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris. This retrospective study included patients initially diagnosed with stable angina pectoris. Patients were randomly divided into either the training or test dataset at an 8 : 2 ratio. Radiomics features were extracted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. A total of 158 patients were included in the analysis. The SVM radiomics model performed well in predicting vulnerable plaques, with AUC values of 0.977 and 0.875 for the training and test cohorts, respectively. With optimal cutoff values, the radiomics model showed accuracies of 0.91 and 0.882 in the training and test cohorts, respectively. Although further larger population studies are necessary, this novel CCTA radiomics model may identify vulnerable plaques in patients with stable angina pectoris.

The Value of Aortic Volume and Intraluminal Thrombus Quantification for Predicting Aortic Events after Endovascular Thoracic Aneurysm Repair

Objectives: To assess the ability of the aortic aneurysm volume (AAV), aneurysmal lumen volume (ALV), and aneurysmal thrombus volume (ATV) to predict the need for aortic reintervention when using the maximal aortic diameter as a reference. Methods: This monocentric retrospective study included 31 consecutive patients who underwent successful thoracic endovascular aortic repair (TEVAR) to treat an atheromatous thoracic aortic aneurysm. All patients underwent clinical and computed tomography angiography (CTA) for 3 years after TEVAR. The patients were categorized into group 0 if no aortic reintervention was required during the follow-up period and categorized into group 1 if they experienced a type I or III endoleak or aneurysm diameter increase requiring intervention. The maximum aneurysm sac diameter and the AAV, ALV, and ATV were calculated using CTA images obtained preoperatively (T0) and at 6–12 months (T1), 24 months (T2), and 36 months (T3) postoperatively, and their changes over time were analyzed. Correlations between diameter and changes in AAV, ALV, and ATV were assessed, and the association between diameter and volume changes and reintervetion was examined. The cutoff values for predicting the need for reintervention was determined using a receiver operating characteristic (ROC) curve. The accuracy of volume change versus diameter change for predicting the need for reintervention was analyzed. Results: There were no significant differences in terms of the mean aneurysm diameter or AAV, ALV or ATV between the groups at preoperative CTA or after one year of follow-up imaging. The mean ATV was higher in group 1 than in group 0 at 2 years (187.6 ± 86.3 mL vs. 114.7 ± 64.7 mL; p = 0.057) and after 3 years (195.0 ± 86.7 mL vs. 82.1 ± 39.9 mL; p = 0.013). The maximal diameter was greater in group 1 than in group 0 at 3 years (67.3 ± 9.5 mm vs. 55.3 ± 12.6 mm; p = 0.044). The rate of AAV change between T0 and T1 was significantly higher in group 1 (7 ± 4.5%) than in group 0 (−6 ± 6.8%; p < 0.001). The rate of ATV change between T1-T3 was significantly higher in group 1 than in group 0 (34 ± 40.9% vs. −13 ± 14.4% (p = 0.041)); similar results were observed for the rate of ATV change between T2 and T3 (27 ± 50.1% for group 1 vs. −8 ± 49.5% in group 0 (p < 0.001)). According to our multivariate analysis, the annual growth rate for AAV between T0 and T1 was the only independent factor that was significantly associated with aortic reintervention (area under the curve (AUC) = 0.84, OR = 1.57, p = 0.025; optimal cutoff +0.4%). An increase in the annual growth rate of the ATV between T0 and T3 was independently associated with the need for aortic reintervention (area under the curve (AUC) = 0.90, OR = 1.11, p = 0.0347; optimal cutoff +10.1%). Conclusions: Aortic volume analysis can predict the need for aortic reintervention more accurately and earlier than maximal aortic diameter.

Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.

Anatomic Study of Vascular Supply of the External Ear Using CT Angiography in Congenital Microtia

Background: The morphology of the arteries of the external ear on the affected side of congenital microtia differs from normal. The present study aimed to use computed tomography angiography (CTA) to describe the anatomic variations of arteries in microtia and provide theoretical guidance for the first stage of autologous auricular reconstruction by the 2-stage method. Methods: Ten patients with unilateral microtia from May 2021 to August 2021 were included. Computed tomography angiography and 3-dimensional reconstruction were used to analyze the supply and branches of the main arteries of the auricle. The number of the superficial temporal artery (STA) and posterior auricular artery (PAA) branches to the auricle, vessel diameter, and the presence or absence of the STA and PAA branches were documented. The skin flap and incision were designed combined with the anatomic of auricular arteries. Results: The blood supply of the auricle mainly came from the STA and PAA. The STA’s preauricular branch and PAA’s posterior auricular branch were absent to varying degrees, and the middle branch was more prominent. The average diameter of the STA on the healthy auricle was 3.07±0.96 mm, and the average diameter of the PAA was 1.72±0.50 mm. The average diameter of the STA on the microtia auricle was 2.65±0.42 mm, and the average diameter of the PAA was 1.53±0.67 mm. There was a statistically significant difference in the diameter of STA between the healthy auricle and the microtia auricle (P=0.006). However, there was no significant difference in the diameter of the PAA between the healthy auricle and the microtia auricle (P=0.112). The skin flap and incision were designed and combined with the preoperative CTA images, and no flap necrosis was observed in all patients. Conclusion: The vascular distribution of arteries in microtia patients was clearly and accurately assessed by CTA. In our experience, the data and detailed imaging were useful in designing skin flaps and incisions during the first stage of autologous auricular reconstruction by the 2-stage method.

Prediction model based on artificial intelligence for identifying risk of coronary atherosclerotic heart disease in computed tomography

ObjectiveTo analyze the application value of artificial intelligence (AI) in coronary computed tomography angiography (CCTA) image processing and diagnosis of coronary atherosclerotic heart disease (CHD). MethodsA total of 80 patients with suspected CHD in our hospital were selected for CCTA examination and blood lipid examination. The convolutional neural networks (CNN) model of coronary artery plaque detection was constructed, and the data set was randomly divided into training set and test set after pretreatment of lipid characteristics and image characteristics. The prediction efficiency and accuracy of the model were evaluated. ResultsIn the data set, the lipid indexes LDL-C, TC, and TG of patients in the CHD group were significantly higher than those in the Non-CHD group (P < 0.05). The average processing and diagnosis time of the AI model was (187.19 ± 18.79) s, which was significantly shorter than the average time of doctors (989.07 ± 50.40) s, and the difference was statistically significant (P < 0.05). There was no significant difference in the detection of calcified plaque, non-calcified plaque, and mixed plaque between doctors and AI models (P > 0.05). However, 5 plaques were misdiagnosed in the AI model (3.38%). The area under the curve (AUC) value of the CNN recognition model-based AI and manual recognition of doctors for the CHD were 0.870 (95% CI: 0.698–0.931) and 0.870 (95% CI: 0.691–0.926) (P < 0.001). ConclusionAI integrated with lipid parameters has certain clinical value in CCTA image processing efficiency and plaque diagnosis, and can be used as an effective auxiliary tool to analyze and diagnose CHD.

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = -0.44 and -0.56 to -0.29 for the vascular angle, and r = -0.19 and -0.34 to -0.02 for the diameter between the subclavian artery and the aneurysm (Fig.3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images.

Ultra-High resolution coronary CT angiography on Photon-Counting detector CT: Bi-Centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements

PurposeTo assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. MethodA dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. ResultsIncreasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). ConclusionsThe QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

Left posterior cerebral artery occlusion following minor head trauma: a case report

A 17-year-old male patient who received a punch behind the left ear 2 days ago, was admitted to the emergency room with a headache and dizziness. His neurological examination was normal. Brain CT images demonstrated diffuse hypodense areas and sulcus deletion in the left occipital lobe. On MR images, diffusion restriction for acute ischemia in the left PCA watershed area was determined, and hospitalization was recommended. However, he and his relatives left the hospital. Through the electronic information system called “e-nabız”, it was learned that the patient was hospitalized in an external center and treated with antiplatelet and anticoagulant regimens for ten days due to an acute thrombus at the left PCA P1 distal-P2 level observed on the brain CT and MR angiography images. In our clinic, his neurological examination was completely normal thirty days later. Still, a visual field examination revealed a total quadrianopsy in the upper temporal pole on the right and left eyes. An encephalomalacia in the left PCA watershed area was observed on CT images. The findings of this case showed that minor head trauma in children can cause large vessel occlusion and therefore close follow-up of these children for a certain time would be appropriate.

Anchura del bulbo de la arteria carótida interna: un nuevo parámetro potencial para la predicción de enfermedades vasculares cerebrales

ObjectiveTo investigate the relationship between the width of the internal carotid artery (ICA) bulb and cerebral vascular diseases including stroke and intracranial aneurysms. Material and methodsIn total 300 patients who had supra-aortic computed tomography angiography (CTA) were enrolled in this study from 2015 to 2021. The study groups consisted of 100 ischemic stroke patients, 100 patients with intracranial aneurysms, and 100 control subjects. The intracranial aneurysm patient group was divided into two subgroups according to the presence of subarachnoid hemorrhage (SAH). The largest diameters of the ICA C1 (cervical) and C2 (petrous) segments in all individuals were measured bilaterally on CTA images. The ICA diameter ratios of the cases were measured using the formula (C1-C2)/C1. The relationship between the age and ICA vessel analysis was evaluated as well. ResultsThe mean ICA bulb width values in the ischemic stroke patient group and the intracranial aneurysm patient group were significantly higher than the control group (P<.001). The ICA C1 and C2 segment diameter values and ICA diameter ratio were smaller in the intracranial aneurysm patients with SAH than those who had not (P= 0.7). There was a statistically significant but weak relationship between the age and ICA diameter ratios in all study groups (R-squared value of 0.26, P= 0.03). ConclusionICA bulb width is a parameter that can be easily evaluated with neuroimaging modalities and is a successful method that may be used for predicting the risk of ischemic stroke or the presence of an intracranial aneurysm.

Prediction of small intracranial aneurysm rupture status based on combined Clinical–Radiomics model

BackgroundAccumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors. MethodsA total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical–radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical–radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis. ResultsA combined clinical–radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757–0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645–0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625–0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model. ConclusionOur study demonstrated the effectiveness of a clinical–radiomics model for predicting rupture status in small aneurysms.

CT Perfusion Derived rCBV < 42% Lesion Volume Is Independently Associated with Followup FLAIR Infarct Volume in Anterior Circulation Large Vessel Occlusion.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman's rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Enhanced acrylic gauge with five eccentric circles for optimizing CT angiography spatial resolution via Taguchi's methodology.

Cerebral examination via CTA is always the first choice for patients with unexpected brain injury or different types of brain lesions to detect ruptured hemangiomas, vascular infarcts, or other brain tissue lesions. This study innovated the acrylic gauge with five eccentric circles for computed tomography angiography (CTA) analysis to optimize the spatial resolution via Taguchi's methodology. The customized gauge was revised from the V-shaped slit gauge and transferred into five eccentric circles' slit gauge. The gauge was assembled with another six acrylic layers to simulate the human head. Taguchi's L18 orthogonal array was adopted to optimize the spatial resolution of CTA imaging quality. In doing so, six essential factors of CTA are kVp, mAs, spiral rotation pitch, FOV, rotation time of the CT and reconstruction filter, and each factor has either two or three levels to organize into eighteen combinations to simulate the full factor combination of 486 (21 × 35 = 486) times according to Taguchi's recommendation. Three well-trained radiologists ranked the gauge's 18 CTA scanned imaging qualities according to contrast, sharpness, and spatial resolution and derived the unique fish-bone-plot of six factors for further analysis. The optimal factor combination of CTA was proven by follow-up verification and ANOVA to obtain this study's dominant or minor factor. The optimal factor combination of CTA was A2 (120 kVp), B3 (200 mAs), C1 (Pitch 0.6), D2 (FOV 220 mm2), E1 (rotation time 0.33 s), and F3 (Brain sharp, UC). Furthermore, deriving a quantified MDD (minimum detectable difference) to imply the spatial resolution of CTA, a semiauto profile analysis program run in MATLAB and OriginPro was recommended to evaluate the MDD and to suppress the manual error in calculation. Eventually, the derived MDDs of the conventional and optimal factor combinations of CTA were 2.35 and 2.26 mm, respectively, in this study. Taguchi's methodology was found applicable for quantifying the CTA imaging quality in practical applications.

Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection

PurposeThis study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute uncomplicated Stanford type B aortic dissection (uTBAD) undergoing initial thoracic endovascular aortic repair (TEVAR). MethodsWe retrospectively evaluated 369 patients treated with TEVAR for acute uTBAD from January 2015 to December 2022. A three-dimensional (3D) deep convolutional neural network (CNN) automated radiomic feature extraction from CTA images. Feature selection, using Analysis of Variance (ANOVA) and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, refined a radiomic score (Rad-Score). This score, alongside clinical parameters, was modelled via Extreme Gradient Boosting (XGBoost) analysis. Model calibration was assessed by calibration curves. ResultsThe integration of the Rad-Score with clinical factors including albumin and C-reactive protein levels moderately enhanced predictive efficiency, exhibiting an area under the curve (AUC) of 1.000 (95%CI, 1.000–1.000) in the training cohort and 0.990 (95%CI, 0.966–1.000) in the internal validation cohort. In an independent validation cohort from another hospital, the combined model yielded an AUC of 0.985 (95%CI, 0.965–1.000), with an accuracy, precision, sensitivity, and specificity of 0.92, 0.92, 0.94, and 0.91, respectively. ConclusionsThe synergistic application of deep learning-based radiomics from CTA and clinical indicators holds promise for anticipating AEs post-initial thoracic endovascular aortic repair in patients with acute uTBAD. The clinical utility of the constructed combined model, offering prognostic foresight during follow-up, has been substantiated.

Internal carotid artery bulb width: a novel potential parameter for the prediction of cerebral vascular diseases

ObjectiveTo investigate the relationship between the width of the internal carotid artery (ICA) bulb and cerebral vascular diseases including stroke and intracranial aneurysms. Material and methodsIn total 300 patients who had supra-aortic computed tomography angiography (CTA) were enrolled in this study from 2015 to 2021. The study groups consisted of 100 ischemic stroke patients, 100 patients with intracranial aneurysms, and 100 control subjects. The intracranial aneurysm patient group was divided into two subgroups according to the presence of subarachnoid hemorrhage (SAH). The largest diameters of the ICA C1 (cervical) and C2 (petrous) segments in all individuals were measured bilaterally on CTA images. The ICA diameter ratios of the cases were measured using the formula C1-C2C1. The relationship between the age and ICA vessel analysis was evaluated as well. ResultsThe mean ICA bulb width values in the ischemic stroke patient group and the intracranial aneurysm patient group were significantly higher than the control group (p < 0.001). The ICA C1 and C2 segment diameter values and ICA diameter ratio were smaller in the intracranial aneurysm patients with SAH than those who had not (p = 0.7). There was a statistically significant but weak relationship between the age and ICA diameter ratios in all study groups (R-squared value of 0.26, p = 0.03). ConclusionICA bulb width is a parameter that can be easily evaluated with neuroimaging modalities and is a successful method that may be used for predicting the risk of ischemic stroke or the presence of an intracranial aneurysm.

Early and mid-term results after endovascular repair of thoracoabdominal aortic aneurysms using the off-the-shelf multibranched t-Branch device: a national multi-center study

Objective: The multibranched off-the-shelf Zenith® t-Branch (Cook Medical, Bloomington, IN) device is commonly chosen for endovascular repair of thoracoabdominal aortic aneurysms. The aim of this study was to report early and mid-term outcomes in all patients treated with the t-Branch in Norway; Design and Methods: A retrospective multicenter study with Norwegian centers performing complex endovascular aortic repair was undertaken. T-Branch patients from 2014 to 2020 were included. All postoperative computed tomography angiography images were reviewed, and demographic, anatomical, perioperative and follow-up data were analyzed; Results: Seventy patients were treated in a single-step (n = 55) or staged (n = 15) procedure. Symptomatic presentation was seen in 20 patients, six of which had a contained rupture. Technical success was 87% (n = 59), with failures caused by unsuccessful bridging of target vessels (n = 4), target vessel bleeding (n = 3), persisting type 1c endoleak (n = 1) and t-Branch malrotation (n = 1). 30-day mortality was 9% (n = 6) and was associated with high BMI (p = .038). The spinal cord ischemia rate was 21% (n = 15) and was associated with type II aneurysms (OR 5.4, 95% CI 1.1–26.7, p = .04), smoking (OR 6.0, 95% CI 1.3–27.6, p = .02) and intraoperative blood loss (OR 1.1, 95% CI 1.0–1.3, p = .01). Survival at one, two and three years was 84 ± 4%, 70 ± 6% and 67 ± 6%, respectively. Freedom from aortic-related reinterventions at one, two and three years was 80 ± 5%, 65 ± 7% and 50 ± 8%, respectively; Conclusion: The study showed low early mortality (9%) and satisfactory mid-term survival. Technical success was achieved in acceptable 87% of procedures. The rate of spinal cord ischemia was high, occurring in 21% of patients.

Impact of Smoking on Coronary Volume-to-Myocardial Mass Ratio: An ADVANCE Registry Substudy.

Purpose To examine the relationship between smoking status and coronary volume-to-myocardial mass ratio (V/M) among individuals with coronary artery disease (CAD) undergoing CT fractional flow reserve (CT-FFR) analysis. Materials and Methods In this secondary analysis, participants from the ADVANCE registry evaluated for suspected CAD from July 15, 2015, to October 20, 2017, who were found to have coronary stenosis of 30% or greater at coronary CT angiography (CCTA) were included if they had known smoking status and underwent CT-FFR and V/M analysis. CCTA images were segmented to calculate coronary volume and myocardial mass. V/M was compared between smoking groups, and predictors of low V/M were determined. Results The sample for analysis included 503 current smokers, 1060 former smokers, and 1311 never-smokers (2874 participants; 1906 male participants). After adjustment for demographic and clinical factors, former smokers had greater coronary volume than never-smokers (former smokers, 3021.7 mm3 ± 934.0 [SD]; never-smokers, 2967.6 mm3 ± 978.0; P = .002), while current smokers had increased myocardial mass compared with never-smokers (current smokers, 127.8 g ± 32.9; never-smokers, 118.0 g ± 32.5; P = .02). However, both current and former smokers had lower V/M than never-smokers (current smokers, 24.1 mm3/g ± 7.9; former smokers, 24.9 mm3/g ± 7.1; never-smokers, 25.8 mm3/g ± 7.4; P < .001 [unadjusted] and P = .002 [unadjusted], respectively). Current smoking status (odds ratio [OR], 0.74 [95% CI: 0.59, 0.93]; P = .009), former smoking status (OR, 0.81 [95% CI: 0.68, 0.97]; P = .02), stenosis of 50% or greater (OR, 0.62 [95% CI: 0.52, 0.74]; P < .001), and diabetes (OR, 0.67 [95% CI: 0.56, 0.82]; P < .001) were independent predictors of low V/M. Conclusion Both current and former smoking status were independently associated with low V/M. Keywords: CT Angiography, Cardiac, Heart, Ischemia/Infarction Clinical trial registration no. NCT02499679 Supplemental material is available for this article. © RSNA, 2024.

Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography

Rationale and objectivesRadiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methodsA total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. ResultsThe SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer–Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. ConclusionsDeep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

756 A 3D Deep-learning Based Algorithm for Cerebral Vessel and Aneurysm Segmentation From CTA Images

INTRODUCTION: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. METHODS: Using 50 paired CTA-DSA images, we trained (n = 27), validated (n = 3) and tested (n = 20) a deep-learning model (3D-DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based co-registration algorithm was used for registration and up-sampling of CTAs to paired DSAs. Segmented vessels from DSAs were used as the ground-truth. Accuracy of the model was evaluated using conventional metrics (dice similarity coefficient-DSC) and vessel-specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 experts detected and segmented IAs. For each rater, we recorded the rate of IA detection. For detected IAs, raters segmented IAs and calculated morphology parameters to quantify the differences in IA segmentation by raters to DeepMedic. The rater agreements were assessed using Krippendorf’s alpha. RESULTS: In testing, the DeepMedic model yielded a DSC and CAL of 0.971 ± 0.007 and 0.868 ± 0.008, respectively. The model delineated all IAs with errors &lt;10% for all IA morphometrics. Conversely, average IA detection accuracy from raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA cavernous region and were significantly smaller). Error rates for IA morphometrics in rater-segmentations were significantly higher than in DeepMedic-segmentations, particularly for neck (p = 0.003), and surface-area (p = 0.003). For IA morphology, agreement between the raters was acceptable for metrics except UI (α=0.36) and NSI (α=0.69). The agreement between DeepMedic and ground truth was consistently higher. CONCLUSIONS: Our CTA segmentation algorithm provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study

Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation. We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases. The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001). This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases. National Natural Science Foundation of China. For the Chinese translation of the abstract see Supplementary Materials section.

Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion

BackgroundAmong patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCTischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion. MethodsAI-QCTischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15O-H2O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCTischemia. Absolute stress myocardial blood flow ≤2.3 ​ml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4–8.3] years. Results662 of 768 (86%) patients had conclusive AI-QCTischemia result. In patients with normal 15O-H2O-PET perfusion, an abnormal AI-QCTischemia result (n ​= ​147/331) vs. normal AI-QCTischemia result (n ​= ​184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17–5.21, p ​= ​0.018). This did not pertain to patients with abnormal 15O-H2O-PET perfusion (abnormal AI-QCTischemia result (n ​= ​269/331) vs. normal AI-QCTischemia result (n ​= ​62/331); adjusted HR 1.09, 95% CI 0.58–2.02, p ​= ​0.794) (p-interaction ​= ​0.039). ConclusionAmong patients with obstructive CAD on coronary CTA referred for downstream 15O-H2O-PET perfusion imaging, AI-QCTischemia showed incremental prognostic value among patients with preserved perfusion by 15O-H2O-PET imaging, but not among those with reduced perfusion.