Association Between Carotid Artery Perivascular Fat Density and Intraplaque Hemorrhage

A novel imaging biomarker for prediction of cerebrovascular ischemic events: Pericarotid fat density

BACKGROUND

To investigate the relationship between pericarotid fat density measured in carotid CTA and vulnerable carotid plaque.

METHODS

This retrospective study included 374 participants who underwent carotid CTA between June 1, 2021, and December 1, 2021 (234 males, median age 68 years [interquartile range: 61-75]). Two groups, symptomatic and asymptomatic, were defined based on either diffusion-weighted MRI or a clinical history of acute ischemia or TIA within 6 months before or after CTA. The relationship between pericarotid fat density and cerebrovascular ischemic events was assessed using receiver operating characteristic analysis and binary logistic regression analysis.

RESULTS

In the symptomatic group (n = 135), mean pericarotid fat density (-63.3 ± 21.7 vs. -81.7 ± 16.9 HU, respectively; p < 0.001) and median maximum plaque thickness (4 [interquartile range: 3-6] vs. 3.7 [interquartile range: 2.6-4.7] mm, respectively; p = 0.002) were higher, while plaque density (42.1 ± 19.6 vs. 50.6 ± 20.4 HU, respectively; p = 0.001) was lower compared to the asymptomatic group. Pericarotid fat density (OR: 1.038, 95% CI: 1.023-1.053, p < 0.001) was identified as an independent predictor for symptomatic patients. The optimal cut-off value for pericarotid fat density predicting symptomatic patients was estimated as -74 HU (area under the curve: 0.753, 95% CI:0.699-0.808, p < 0.001). Inter-reader agreement for pericarotid fat density was found to be almost perfect (intraclass correlation coefficient: 0.818, 95% CI: 0.770-0.856, p < 0.001).

CONCLUSION

Pericarotid fat density may serve as an imaging biomarker in predicting acute cerebrovascular ischemic events.

Pericarotid Fat as a Marker of Cerebrovascular Risk

Vascular inflammation is widely recognized as an important factor in the atherosclerotic process, particularly in terms of plaque development and progression. Conventional tests, such as measuring circulating inflammatory biomarkers, lack the precision to identify specific areas of vascular inflammation. In this context, noninvasive imaging modalities can detect perivascular fat changes, serving as a marker of vascular inflammation. This review aims to provide a comprehensive overview of the key concepts related to perivascular carotid fat and its pathophysiology. Additionally, we examine the existing literature on the association of pericarotid fat with features of plaque vulnerability and cerebrovascular events. Finally, we scrutinize the advantages and limitations of the noninvasive assessment of pericarotid fat.

Relationship Between Pericarotid Fat Density and Pathology-Based Carotid Plaque Risk Characteristics

OBJECTIVES

In this study, the authors aimed to evaluate the relationship between pericarotid fat density (PFD) and pathologic carotid plaque risk characteristics.

METHODS

The authors retrospectively evaluated 58 patients (mean age: 66.66 ± 7.26 y, 44 males) who were subjected to both carotid endarterectomy and carotid artery computed tomography angiography (CTA) at the authors' institution. The computed tomography values of the adipose tissue around the most severe stenosis carotid artery were measured, and the removed plaques were sent to the Department of Pathology for American Heart Association (AHA) classification. The Wilcoxon signed-rank test was used to detect the difference in PFD values between the operative and nonoperative sides. According to carotid plaque risk characteristics, the associations between PFD and 4 different risk characteristic subgroups were analyzed. The Student t test and χ2 test were used to compare differences between different risk subgroups. Receiver operating characteristic curve analysis was used to evaluate the predictive efficacy of PFD for carotid plaque risk characteristics.

RESULTS

The operative side had higher mean Hounsfield units (HU) values compared with the nonoperative side (P < 0.001). The AHA VI and the intraplaque hemorrhage (IPH) subgroups had higher mean HU values compared with the non-AHA VI and the non-IPH subgroups (P < 0.05). Male patients presented with IPH more than female patients (P = 0.047). The results of receiver operating characteristic curve analysis showed that the mean HU value (operative side; area under the curve: 0.729, Sensitivity (SE): 59.26%, Specificity (SP): 80.65%, P = 0.003) had a certain predictive value for diagnosing high-risk VI plaques. Pericarotid fat density ≥ -68.167 HU is expected to serve as a potential cutoff value to identify AHA VI and non-AHA VI subgroups.

CONCLUSION

PFD was significantly associated with vulnerable plaques, high-risk AHA VI plaques, and IPH, which could be an indirect clinical marker for vulnerable plaques.

Carotid Artery Perivascular Adipose Tissue Density and Response to Intravenous Tissue Plasminogen Activator in Acute Ischemic Stroke

The importance of Carotid Artery Perivascular Adipose Tissue Density (CAPATd), a parameter that can be readily evaluated on emergency computed tomographic angiography (CTA), in acute stroke has not been adequately clarified. We created exploratory logistic regression models to detect the interaction between the effect of CAPATd and intravenous (IV) tissue plasminogen activator (tPA) in 174 patients (mean age 71 ± 14 years, 94 women) with acute ischemic stroke treated with IV-tPA alone. The CAPATd-average mean (-60.6 ± 18.7 vs -89.8 ± 25.3 Hounsfield units (HU), P = .002) and CAPATd-maximum (14.8 ± 68.9 vs -20.5 ± 39.8 HU, P = .020) values were higher on the ipsilateral side of carotid artery stenosis >60%. CAPATd-maximum ipsilateral emerged as an independent predictor for both modified Rankin's Score 0-2 (52%) [exp(β) = .984] and mRS 0-1 outcome (32%) [exp(β) = .828] in addition to admission National Institutes of Health Stroke Scale, age and carotid plaque burden. CAPATd-maximum ipsilateral was acceptably accurate (Area under the Receiver operating characteristic Curve was .607, P = .0109 for mRS 0-2 and .613, P = .0102 for mRS 0-1). Ipsilateral CAPATd ≥ -25 HU predicted both mRS >3 and mRS >2 with usable sensitivity (59.8% and 66.07%) and specificity (63.6% and 59.68%). In conclusion, higher maximum CAPATd measured on emergency CTA indicates poorer functional prognosis in acute stroke patients treated with IV-tPA.

Pericarotid Fat Stranding at Computed Tomography Angiography: A Marker of the Short-Term Prognosis of Acute Ischemic Stroke

PURPOSE

Perivascular epicardial fat stranding detected in the coronary computed tomography (CT) angiography is associated with culprit lesions and provides helpful information on the risk of acute coronary syndrome. This study aimed to evaluate the potential clinical significance of pericarotid fat stranding (PCFS) and investigate the association between PCFS and short-term prognosis in acute stroke using head and neck CT angiography (CTA).

METHODS

This study included 80 patients (mean age, 69.69 ± 11.03; 58 men) who underwent both head and neck CTA and magnetic resonance imaging within a 1-week period. Baseline characteristics, pericarotid adipose tissue attenuation, plaque characteristics, ischemic penumbra, infarct core volume, infarct core growth rate (CGR), and the grade of collateral status were recorded and compared between a PCFS group and a non-PCFS group. Data were compared using the 2-sample t test, Mann-Whitney U test, Fisher exact test, and Spearman rank correlation analysis.

RESULTS

We found that patients with PCFS had a significantly higher pericarotid adipose tissue density than patients without PCFS (-55.75 ± 5.53 vs -65.82 ± 9.65, P < 0.001). Patients with PCFS showed a larger infarct core volume (166.43 ± 73.07 vs 91.43 ± 55.03, P = 0.001) and faster CGR (39.57 ± 12.01 vs 19.83 ± 32.77; P < 0.001), and the frequency of adverse prognosis was more significant than in control participants (83.33% vs 19.11%).

CONCLUSIONS

Individuals with PCFS showed higher CGR, which was substantially related to worse outcomes in patients with acute stroke with ipsilateral carotid atherosclerosis. Recognition of PCFS may help predict stroke prognosis and allow doctors to take early action to improve patient prognosis.

Carotid artery perivascular adipose tissue on magnetic resonance imaging: a potential indicator for carotid vulnerable atherosclerotic plaque

Background

Magnetic resonance imaging (MRI) has the potential in assessing the inflammation of perivascular adipose tissue (PVAT) due to its excellent soft tissue contrast. However, evidence is lacking for the association between carotid PVAT measured by MRI and carotid vulnerable atherosclerotic plaques. This study aimed to investigate the association between signal intensity of PVAT and vulnerable plaques in carotid arteries using multi-contrast magnetic resonance (MR) vessel wall imaging.

Methods

In this cross-sectional study, a total of 104 patients (mean age, 64.9±7.0 years; 86 men) with unilateral moderate-to-severe atherosclerotic stenosis referred to carotid endarterectomy (CEA) were recruited from April 2018 to December 2020 at Department of Neurosurgery of Peking University Third Hospital. All patients underwent multi-contrast MR vessel wall imaging including time-of-flight (ToF) MR angiography, black-blood T1-weighted (T1w) and T2-weighted (T2w) and simultaneous non-contrast angiography and intraplaque hemorrhage (IPH) imaging sequences. Patients with contraindications to endarterectomy or MRI examinations were excluded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of PVAT were measured on ToF images and vulnerable plaque characteristics including IPH, large lipid-rich necrotic core (LRNC), and fibrous cap rupture (FCR) were identified. The SNR and CNR of PVAT were compared between slices with and without vulnerable plaque features using Mann-Whitney U test and their associations were analyzed using the generalized linear mixed model (GLMM).

Results

Carotid artery slices with IPH (30.93±14.56 vs. 27.34±10.02; P<0.001), FCR (30.35±13.82 vs. 27.53±10.37; P=0.006), and vulnerable plaque (29.15±12.52 vs. 27.32±10.05; P=0.016) had significantly higher value of SNR of PVAT compared to those without. After adjusting for clinical confounders, the SNR of PVAT was significantly associated with presence of IPH [odds ratio (OR) =0.627, 95% confidence interval (CI): 0.465-0.847, Puncorr=0.002, PFDR=0.016] and vulnerable plaque (OR =0.762, 95% CI: 0.629-0.924, Puncorr=0.006, PFDR=0.020). However, no significant association was found between the CNR of PVAT and presence of vulnerable plaque features (all P>0.05).

Conclusions

The SNR of carotid artery PVAT measured by ToF MR angiography is independently associated with vulnerable atherosclerotic plaque features, suggesting that the signal intensity of PVAT might be an effective indicator for vulnerable plaque.

Computed tomography angiography-based radiomics model to identify high-risk carotid plaques

Background

Extracranial atherosclerosis is one of the major causes of stroke. Carotid computed tomography angiography (CTA) is a widely used imaging modality that allows detailed assessments of plaque characteristics. This study aimed to develop and test radiomics models of carotid plaques and perivascular adipose tissue (PVAT) to distinguish symptomatic from asymptomatic plaques and compare the diagnostic value between radiomics models and traditional CTA model.

Methods

A total of 144 patients with carotid plaques were divided into symptomatic and asymptomatic groups. The traditional CTA model was built by the traditional radiological features of carotid plaques measured on CTA images which were screened by univariate analysis and multivariable logistic regression. We extracted and screened radiomics features from carotid plaques and PVAT. Then, a support vector machine was used for building plaque and PVAT radiomics models, as well as a combined model using traditional CTA features and radiomics features. The diagnostic value between radiomics models and traditional CTA model was compared in identifying symptomatic carotid plaques by Delong method.

Results

The area under curve (AUC) values of traditional CTA model were 0.624 and 0.624 for the training and validation groups, respectively. The plaque radiomics model and PVAT radiomics model achieved AUC values of 0.766, 0.740 and 0.759, 0.618 in the two groups, respectively. Meanwhile, the combined model of plaque and PVAT radiomics features and traditional CTA features had AUC values of 0.883 and 0.840 for the training and validation groups, respectively, and the receiver operating characteristic curves of combined model were significantly better than those of traditional CTA model in the training group (P<0.001) and validation group (P=0.029).

Conclusions

The combined model of the radiomics features of carotid plaques and PVAT and the traditional CTA features significantly contributes to identifying high-risk carotid plaques compared with traditional CTA model.

Associations between preprocedural carotid artery perivascular fat density and early in-stent restenosis after carotid artery stenting

Objectives

This study investigated the association between perivascular fat density (PFD) via preoperative computed tomographic angiography (CTA) and early in-stent restenosis (ISR) after carotid artery stenting (CAS).

Methods

We retrospectively evaluated 248 consecutive patients who had undergone initial CAS and received a preoperative cervical CTA examination between January 2019 and October 2020. The patients were categorized into two according to whether they sustained ISR during the 2 years postoperative follow-up period. Correlations between PFD and ISR were assessed, and multivariate regression for evaluating predictors of ISR was conducted. Receiver operating characteristic (ROC) curves were used to determine the cutoff value for the PFD.

Results

A total of 181 eligible patients (mean age 61.25 ± 10.35 years, 57 male) were enrolled. The ISR group had a higher proportion of closed-cell stents (48.8% versus 27.5%; p = 0.009) and a greater degree of residual stenosis (28[20,33] % versus 20[14.75,30] %; p < 0.001) than the non-ISR group. The ISR group had a higher mean HU value of PFD than the non-ISR group on the operated side (-42.26 ± 6.81 versus -59.66 ± 10.75; p < 0.001). The degree of residual stenosis (OR 1.146, 95%CI 1.071-1.226, p < 0.001) and PFD on the operated side (OR1.353, 95%CI 1.215-1.506, p < 0.001) were significantly associated with the ISR.

Conclusions

The occurrence of the early ISR after CAS is associated with a higher PFD on the operated side. The results indicate that PFD is a promising marker to predict the ISR after CAS.

Development and validation of a preoperative nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer: a retrospective analysis

BACKGROUND

In laparoscopic right hemicolectomy for right colon cancer, complete mesocolic excision is a standard procedure that involves extended lymphadenectomy and blood vessel ligation. This study aimed to establish a nomogram to facilitate evaluation of the surgical difficulty of laparoscopic right hemicolectomy based on preoperative parameters.

MATERIALS AND METHODS

The preoperative clinical and computed tomography-related parameters, operative details, and postoperative outcomes were analyzed. The difficulty of laparoscopic colectomy was defined using the scoring grade reported by Escal et al . with modifications. Multivariable logistic analysis was performed to identify parameters that increased the surgical difficulty. A preoperative nomogram to predict the surgical difficulty was established and validated.

RESULTS

A total of 418 consecutive patients with right colon cancer who underwent laparoscopic radical resection at a single tertiary medical center between January 2016 and May 2022 were retrospectively enrolled. The patients were randomly assigned to a training data set ( n =300, 71.8%) and an internal validation data set ( n =118, 28.2%). Meanwhile, an external validation data set with 150 consecutive eligible patients from another tertiary medical center was collected. In the training data set, 222 patients (74.0%) comprised the non-difficulty group and 78 (26.0%) comprised the difficulty group. Multivariable analysis demonstrated that adipose thickness at the ileocolic vessel drainage area, adipose area at the ileocolic vessel drainage area, adipose density at the ileocolic vessel drainage area, presence of the right colonic artery, presence of type III Henle's trunk, intra-abdominal adipose area, plasma triglyceride concentration, and tumor diameter at least 5 cm were independent risk factors for surgical difficulty; these factors were included in the nomogram. The nomogram incorporating seven independent predictors showed a high C-index of 0.922 and considerable reliability, accuracy, and net clinical benefit.

CONCLUSIONS

The study established and validated a reliable nomogram for predicting the surgical difficulty of laparoscopic colectomy for right colon cancer. The nomogram may assist surgeons in preoperatively evaluating risk and selecting appropriate patients.