Identification Of Culprit Lesion Research Articles

CT texture analysis of vertebrobasilar artery calcification to identify culprit plaques.

The aim of this study was to extract radiomic features from vertebrobasilar artery calcification (VBAC) on head computed tomography (CT) images and investigate its diagnostic performance to identify culprit lesions responsible for acute cerebral infarctions. Patients with intracranial atherosclerotic disease who underwent vessel wall MRI (VW-MRI) and head CT examinations from a single center were retrospectively assessed for VBAC visual and textural analyses. Each calcified plaque was classified by the likelihood of having caused an acute cerebral infarction identified on VW-MRI as culprit or non-culprit. A predefined set of texture features extracted from VBAC segmentation was assessed using the minimum redundancy and maximum relevance method. Five key features were selected to integrate as a radiomic model using logistic regression by the Aikaike Information Criteria. The diagnostic value of the radiomic model was calculated for discriminating culprit lesions over VBAC visual assessments. A total of 1,218 radiomic features were extracted from 39 culprit and 50 non-culprit plaques, respectively. In the VBAC visual assessment, culprit plaques demonstrated more observed presence of multiple calcifications, spotty calcification, and intimal predominant calcification than non-culprit lesions (all p < 0.05). In the VBAC texture analysis, 55 (4.5%) of all extracted features were significantly different between culprit and non-culprit plaques (all p < 0.05). The radiomic model incorporating 5 selected features outperformed multiple calcifications [AUC = 0.81 with 95% confidence interval (CI) of 0.72, 0.90 vs. AUC = 0.61 with 95% CI of 0.49, 0.73; p = 0.001], intimal predominant calcification (AUC = 0.67 with 95% CI of 0.58, 0.76; p = 0.04) and spotty calcification (AUC = 0.62 with 95% CI of 0.52, 0.72; p = 0.005) in the identification of culprit lesions. Culprit plaques in the vertebrobasilar artery exhibit distinct calcification radiomic features compared to non-culprit plaques. CT texture analysis of VBAC has potential value in identifying lesions responsible for acute cerebral infarctions, which may be helpful for stroke risk stratification in clinical practice.

Localising culprit artery in inferior STEMI

BackgroundST elevation myocardial infarction (STEMI) represents a cardiac emergency. Time to diagnosis, identification of culprit lesion, and intervention are important. Inferior STEMI represents a dilemma for cardiologists. The territory can...

The role of speckle tracking echocardiography in the identification of culprit lesion in patients with non ST-segment elevation acute coronary syndrome

Abstract INTRODUCTION An early evaluation of patients with non-ST elevation acute coronary syndrome patients (NSTE-ACS) is important to choose the appropriate treatment strategy. In this setting of patients, conventional echocardiographic assessment may reveal normal myocardial kinesis in 25 to 76% of cases. Global and territorial longitudinal strain (GLS and TLS, respectively) may be an early and accurate non-invasive tool for prediction of multivessel CAD in patients with NSTE-ACS. AIM To evaluate the ability of TLS to predict culprit lesions in patients with NSTE-ACS. Material and method We studied 183 patients diagnosed with NSTE-ACS, in our Institution over 2 years of time. Conventional echocardiography and 2D speckle tracking echocardiography (STE) imaging were performed by two experienced echocardiographers, who were blinded to patient characteristics. The TLS was identified as the mean value of the segments'strain as respect to each vessel territory.Coronary angiography was performed in all patients. Significant CAD (luminal stenosis more than 70% in a major epicardial coronary vessel) and culprit lesion were identified and threated by PTCA when appropriate. Results A significant difference between mono- and tri-vessel CAD in the variation of WMSI has been demonstrated. There was a statistically significant difference between both 3-vessels vs 1-vessel disease and 2-vessels vs 1-vessel disease in changing of TLS-LAD, TLS-RCA and TLS-Cx values (p-value &amp;lt;0.001). There was a significant difference between 3-vessels vs 2-vessels disease for TLS-RCA values. There was a statistically significant difference for WMSI-LAD, WMSI-CX and WMSI-RCA values whether the respective artery was involved or not. Variations of TLS were statistically significant both when the territorial tributary artery was involved and also if the artery represented the culprit lesion (p-value TLS-LAD &amp;lt;0.001, TLS-LAD culprit &amp;lt;0.001, TLS-CX&amp;lt;0.001, TLS-cx culprit &amp;lt;0.001, TLS-RCA &amp;lt;0.001, p-value TLS-RCA culprit 0.022). A regression model was performed comparing the variation of WMSI as respect to the variation of WMSI+TLS in the territory of culprit lesions. For WMSI- LAD the OR was 0.94 and for TLS-LAD the OR was 1.19 and the p-value of the addition was 0.001. The OR of WMSI-CX was 1.76 and for TLS-CX the OR was 1.40 and the p- value of the addition was 0.001. The OR of WMSI- RCA was 0.71 and for TLS- RCA the OR was 1.17, the p- value of the addition was 0.019. Conclusion TLS allows an accurate identification of the culprit lesion in patients presenting with NSTE-ACS. TLS can be considered as part of routine echocardiography on top of WMSI in early evaluation for a better clinical assessment in this subset of patients. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Policlinico di Bari

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.

Principles and Clinical Application of Dual-energy Computed Tomography in the Evaluation of Cerebrovascular Disease.

Dual-energy computed tomography (DECT) simultaneously acquires images at two X-ray energy levels, at both high- and low-peak voltages (kVp). The material attenuation difference obtained from the two X-ray energies can be processed by software to analyze material decomposition and to create additional image datasets, namely, virtual noncontrast, virtual contrast also known as iodine overlay, and bone/calcium subtraction images. DECT has a vast array of clinical applications in imaging cerebrovascular diseases, which includes: (1) Identification of active extravasation of iodinated contrast in various types of intracranial hemorrhage; (2) differentiation between hemorrhagic transformation and iodine staining in acute ischemic stroke following diagnostic and/or therapeutic catheter angiography; (3) identification of culprit lesions in intra-axial hemorrhage; (4) calcium subtraction from atheromatous plaque for the assessment of plaque morphology and improved quantification of luminal stenosis; (5) bone subtraction to improve the depiction of vascular anatomy with more clarity, especially at the skull base; (6) metal artifact reduction utilizing virtual monoenergetic reconstructions for improved luminal assessment postaneurysm coiling or clipping. We discuss the physical principles of DECT and review the clinical applications of DECT for the evaluation of cerebrovascular diseases.

Characterization of Noncalcified Coronary Plaques and Identification of Culprit Lesions in Patients With Acute Coronary Syndrome by 64-Slice Computed Tomography

We sought to characterize noncalcified coronary atherosclerotic plaques in culprit and remote coronary atherosclerotic lesions in patients with acute coronary syndrome (ACS) with 64-slice computed tomography (CT). Lower CT density, positive remodeling, and adjacent spotty coronary calcium are characteristic vessel changes in unstable coronary plaques. Of 147 consecutive patients who underwent contrast-enhanced 64-slice CT examination for coronary artery visualization, 101 (ACS; n = 21, non-ACS; n = 80) having 228 noncalcified coronary atherosclerotic plaques (NCPs) were studied. Each NCP detected within the vessel wall was evaluated by determining minimum CT density, vascular remodeling index (RI), and morphology of adjacent calcium deposits. The CT visualized more NCPs in ACS patients (65 lesions, 3.1 +/- 1.2/patient) than in non-ACS patients (163 lesions, 2.0 +/- 1.1/patient). Minimum CT density (24 +/- 22 vs. 42 +/- 29 Hounsfield units [HU], p < 0.01), RI (1.14 +/- 0.18 vs. 1.08 +/- 0.19, p = 0.02), and frequency of adjacent spotty calcium of NCPs (60% vs. 38%, p < 0.01) were significantly different between ACS and non-ACS patients. Frequency of NCPs with minimum CT density <40 HU, RI >1.05, and adjacent spotty calcium was approximately 2-fold higher in the ACS group than in the non-ACS group (43% vs. 22%, p < 0.01). In the ACS group, only RI was significantly different between 21 culprit and 44 nonculprit lesions (1.26 +/- 0.16 vs. 1.09 +/- 0.17, p < 0.01), and a larger RI (> or = 1.23) was independently related to the culprit lesions (odds ratio: 12.3; 95% confidential interval: 2.9 to 68.7, p < 0.01), but there was a substantial overlap of the distribution of RI values in these 2 groups of lesions. Sixty-four-slice CT angiography demonstrates a higher prevalence of NCPs with vulnerable characteristics in patients with ACS as compared with stable clinical presentation.

Identification of Culprit Lesions After Transient Ischemic Attack by Combined 18 F Fluorodeoxyglucose Positron-Emission Tomography and High-Resolution Magnetic Resonance Imaging

Carotid endarterectomy is currently guided by angiographic appearance on the assumption that the most stenotic lesion visible at angiography is likely to be the lesion from which future embolic events will arise. However, risk of plaque rupture, the most common cause of atherosclerosis-related thromboembolism, is dictated by the composition of the plaque, in particular the degree of inflammation. Angiography may, therefore, be an unreliable method of identifying vulnerable plaques. In this study, plaque inflammation was quantified before endarterectomy using the combination of 18F fluorodeoxyglucose positron (FDG)-emission tomography (PET) and high-resolution MRI (HRMRI). Twelve patients, all of whom had suffered a recent transient ischemic attack, had a severe stenosis in the ipsilateral carotid artery, and were awaiting carotid endarterectomy underwent FDG-PET and HRMRI scanning. A semiquantitative estimate of plaque inflammation was calculated for all of the lesions identified on HRMRI. In 7 of 12 patients (58%), high FDG uptake was seen in the lesion targeted for endarterectomy. In the remaining 5 patients, FDG uptake in the targeted lesion was low. In these 5 patients, 3 had nonstenotic lesions identified on HRMRI that exhibited a high level of FDG uptake. All 3 of the highly inflamed nonstenotic lesions were located in a vascular territory compatible with the patients' presenting symptoms. Our data suggest that angiography may not always identify the culprit lesion. Combined FDG-PET and HRMRI can assess the degree of inflammation in stenotic and nonstenotic plaques and could potentially be used to identify lesions responsible for embolic events.