The value of multiphase CT angiography in predicting hemorrhagic transformation after endovascular treatment: the arterial collaterals and venous drainage

Introduction

Endovascular treatment (EVT) has been identified as an effective treatment for patients with acute ischemic stroke (AIS) due to large vessel occlusion in the anterior circulation (1,2). Hemorrhagic transformation (HT) is a common and severe complication after EVT, with an incidence rate of as high as 40% (3), especially the symptomatic intracerebral hemorrhage (sICH), as one of the fatal HT, is associated with early neurological deterioration and high mortality (4), significantly diminishing the benefit of EVT. Therefore, predicting HT in AIS patients before EVT and taking corresponding intervention measures are essential, especially for predicting sICH.

Multiphase computed tomography (CT) angiography (mCTA) is a technique that generates three phases of images of the entire intracranial circulation with the advantage of temporal resolution (5). mCTA images can more accurately evaluate pial arterial filling and venous outflow than single-phase CTA images (6-11). In recent years, studies have confirmed that the better the established arterial collateral circulation (ACC), the lower the risk of HT (12,13). Moreover, recent studies have shown that venous drainage also plays a crucial role in maintaining cerebral blood flow after ischemia. Patients with good outcomes exhibit favorable venous outflow compared to those with poor outcomes (9,10,14). However, studies have neither explored the prediction of stratified HT, i.e., sICH risk, nor the impact of mCTA-based assessment of venous drainage on HT and sICH after EVT.

This study aimed to explore the application value of mCTA in predicting HT and sICH after EVT in patients with anterior circulation AIS and to assess the mCTA’s quantitative predictive ability for HT and sICH based on ACC, superficial venous drainage scores (SVS), and deep venous drainage scores (DVS). We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2664/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The Institutional Review Board of Tianjin Huanhu Hospital approved the study (Clinical Study Approval 2022-072), and individual informed consent for this retrospective analysis was waived.

We collected data of patients with anterior circulation AIS who were admitted to Tianjin Huanhu Hospital and treated with EVT, including intra-arterial thrombolytic therapy and/or mechanical thrombectomy, from April 2020 to December 2023 (Figure 1). The inclusion criteria were as follows: (I) unilateral large vessel occlusion of anterior circulation [distal of the internal carotid artery (ICA), and/or proximal of the middle cerebral artery (MCA)]; (II) had a stroke onset time within 24 h; (III) non-contrast CT (NCCT) and mCTA of the head were acquired before treatment; (IV) follow-up NCCT of the head was acquired within 24–48 hours after EVT. The exclusion criteria were as follows: (I) patients with recurrent stroke (n=6); (II) complicated with posterior circulation stroke (n=4); (III) complicated with vascular malformations or brain tumors (n=4); (IV) lost to follow-up (n=7); (V) poor image quality was insufficient for diagnosis (n=8).

Figure 1 Flowchart of our study. aICH, asymptomatic intracerebral hemorrhage; AIS, acute ischemic stroke; EVT, endovascular treatment; HT, hemorrhagic transformation; sICH, symptomatic intracerebral hemorrhage.

Clinical and imaging assessment

Patients underwent baseline NCCT and mCTA, time from onset to CT scan, baseline National Institutes of Health Stroke Scale (NIHSS) scores, modified Rankin Scale (mRS), and Alberta Stroke Program Early CT Score (ASPECTS). Additionally, we collected clinical data including gender and age, neutrophilic to lymphocyte ratio (NLR), as well as stroke-related risk factors such as a history of hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, smoking, and alcohol consumption.

Moreover, we have recorded and collected the patients’ data during the EVT, including the method of EVT, the number of catheter passes during thrombectomy, modified thrombolysis in cerebral infarction (mTICI) score, occlusion location of the artery, and etiology classification based on the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria.

A follow-up NCCT image was obtained at 24–48 hours after EVT, using the European Cooperative Acute Stroke Study (ECASS II) criteria (15), patients were divided into HT and non-HT groups. HT was defined as an intracranial hemorrhage on a follow-up NCCT. Two neuroradiologists (with more than 10 years of experience) were blinded to clinical information (except for the affected hemisphere) and conducted the assessments. Once a discrepancy occurred, a third neuroradiologist (with more than 20 years of experience) would make a final decision.

NIHSS scores were performed at baseline and at 24–48 hours after EVT. Patients in the HT group have been further divided into the asymptomatic intracerebral hemorrhage (aICH) and sICH subgroups. sICH was defined as any of the following conditions (16): (I) increased the NIHSS score by 4 points than that before worsening; (II) increased NIHSS score by two points in one NIHSS category; and (III) deterioration leading to intubation, hemicraniectomy, external ventricular drain placement, or any other major intervention.

mRS scores were performed at baseline, 7-, and 90-day follow-up by face-to-face communications or telephone interviews. We used the 90-day mRS score as the midterm functional outcomes.

NIHSS and mRS scores were evaluated by two clinicians (with more than 8 years of experience) blinding to all imaging information. Once a discrepancy occurred, a decision was made after discussion.

Imaging protocol

All patients received an NCCT and mCTA scan with a 256-slice spiral CT scanner (Revolution, GE HealthCare, USA). The NCCT was performed from the skull base to the vertex. The scanning parameters were as follows: tube voltage 120 kVp, automatic tube current regulation (200–250 mAs), and 5-mm slice thickness reconstruction.

The mCTA scanning parameters were as follows: tube voltage 100 kV, tube current 445 mA, layer thickness 0.625 mm, adaptive statistical iterative reconstruction 60%, FOV 250 mm × 250 mm, matrix 512×512. The mCTA consisted of three scanning phases: the arterial, venous, and late venous phase, the interval time was 8s. The scanning range of the arterial phase was from the aortic arch to the vertex, and the venous and late venous phases were from the skull base to the vertex. A bolus of contrast agent (Omnipaque 350 mgI/mL, GE HealthCare, Shanghai, China) 45 mL was injected at a rate of 5 mL/s through the right ulnar vein, followed by 30 mL of saline chaser with an injected rate of 5 mL/s. Bolus tracking was set to trigger at 120 HU in the ascending aorta, with a 4-second scan delay.

Image analysis

After scanning, mCTA imaging data were automatically imported to the AW 4.7 workstation and processed with the FastStroke Analysis Software (GE Healthcare, Milwaukee, Wisconsin). Two neuroradiologists (with more than 10 years of experience) were blinded to clinical information (except for the affected hemisphere), analyzed images, and assessed pial arterial filling and venous drainage scores.

Pial arterial filling score based on mCTA was used to assess ACC according to Menon criteria (5), which was a 6-point scale ranging from 0–5. Good ACC was defined as a score of 4–5, and poor ACC was defined as a score of 0–3.

Venous drainage containing superficial and deep veins was assessed by SVS and DVS, respectively. SVS and DVS were evaluated by comparison of the ipsilateral side (side of intracranial arterial occlusion) and contralateral side in each phase, and used a 3-point scale: 0, no opacification on the ipsilateral side; 1, moderate opacification on the ipsilateral side; and 2, similar or increased opacification on the ipsilateral side. Four major superficial veins, which include the vein of Trolard, the vein of Labbe, the superficial middle cerebral vein, and the sphenoparietal sinus, were chosen for SVS assessment because they account for the majority of venous drainage from the MCA territory and show less anatomical variation as compared to other cortical veins (17). The four superficial veins were scored separately and added together, yielding a maximum score of 8 at each phase of mCTA. The internal cerebral vein (ICV) was chosen for DVS assessment because it is easily visible (18). The DVS resulted in a maximum score of 2 at each phase of mCTA. SVS1, SVS2, and SVS3, as well as DVS1, DVS2, and DVS3, represented SVS and DVS in the arterial, venous, and late venous phases, respectively. A representative case is shown in Figure 2.

Figure 2 A 64-year-old man with left MCA occlusion. Axial images of multiphase CTA showing the ACC, SVS, and DVS in the arterial phase, venous phase, and late venous phase of the ipsilateral side compared with the right side. The ACC was poor on the left side. The venous drainage scores of SVS1, SVS2, and SVS3 were 1, 4, and 7, respectively. The venous drainage scores of DVS1, DVS2, and DVS3 were 0, 1, and 2, respectively. ACC, arterial collateral circulation; CTA, computed tomography angiography; DVS, deep venous score; ICV, the internal cerebral vein; MCA, middle cerebral artery; SMCV, the superficial middle cerebral vein; SPS, sphenoparietal sinus; SVS, superficial venous score; VOL, the vein of Labbe; VOT, the vein of Trolard.

Statistical analysis

Statistical analysis was performed using SPSS 22.0 (IBM, USA), R (version 3.4.1, R Foundation for Statistical Computing), and MedCalc (version 12.3.0). The Kolmogorov-Smirnov test was used to analyze normality. If continuous variables were normally distributed, they were presented as mean ± standard deviation. Otherwise, they were presented as median [interquartile range (IQR)]. The independent t-test or Mann-Whitney U test was used to compare groups. Categorical variables were presented as numbers (%), and the χ² or Fisher’s exact test was used to compare groups. A two-tailed P<0.05 was considered statistically significant.

The Kappa test was used to assess the repeatability of ACC, SVS, and DVS based on the mCTA data. κ values were interpreted as follows: 0.80<κ≤1, almost perfect agreement; 0.60<κ≤0.80, substantial agreement; 0.40<κ≤0.60, moderate agreement; 0.20<κ≤0.40, fair agreement; κ≤0.20, slight to no agreement. The kappa values were presented as numbers with a 95% confidence interval (CI).

In this study, we used a multivariate logistic regression to analyze risk factors for HT and sICH. The predictive models of HT and sICH were established using ACC (Model-HT1 and Model-sICH1), SVS and DVS (Model-HT2 and Model-sICH2), and comprehensive parameters (Model-HT3 and Model-sICH3). Model-HT1 and model-sICH1 included clinical variables with P<0.20 in univariate analysis and ACC. Model-HT2 and model-sICH2 included clinical variables with P<0.20 in univariate analysis, SVS, and DVS. Model-HT3 and model-sICH3 included all clinical variables, ACC, SVS, and DVS with P<0.20 in the univariate analysis.

The Hosmer-Lemeshow test was used to assess the goodness-of-fit of predictive models. We analyzed the area under curve (AUC) of the receiver operating characteristic (ROC) and compared the models using the Delong test. Further, we conducted the calibration curve analysis. The clinical utility of the predictive model was assessed by decision curve analysis and clinical impact curve analysis.

Results

Patient characteristics

Finally, 127 patients (100 male, 27 female) were included in this analysis. Patients had a median age of 64 (IQR, 58–69) years, and their median admission NIHSS score was 13.00 (IQR, 10.00–16.00) points. The median time from symptom onset to CT was 11.00 (IQR, 8.00–17.00) hours. A total of 46 patients developed HT after EVT, among which 32 patients had aICH and 14 patients had sICH (The information of 14 cases of sICH is listed in the Table S1, and the NCCT images of two cases of sICH are presented in the Figure S1), respectively.

Significant differences in Patients’ age and history of hyperlipidemia between non-HT and HT groups (P<0.05). The 90-day mRS score in the HT group was higher than non-HT group (P<0.05). There was a lower percentage of good outcomes (90-day mRS, 0–2) in the HT group than non-HT group (P<0.05).

The time from symptom onset to CT significantly differed between the aICH and sICH subgroups (P<0.05). There was a lower percentage of good outcomes (90d mRS, 0–2) in the sICH subgroup than aICH subgroup (P<0.05). No significant differences were observed in other clinical variables (P>0.05). The distribution of detailed clinical characteristics in the non-HT and HT-groups, as well as the aICH and sICH subgroups, is shown in Table 1.

Comparison of the mCTA parameters between the non-HT and HT groups, and between the aICH and sICH subgroups

The comparison of the mCTA parameters between the non-HT and HT groups, and between the aICH and sICH subgroups is also shown in Table 1. The HT group had a higher ratio of poor ACC than the non-HT group (P<0.05). SVS1, SVS2, and SVS3 significantly differed between the non-HT and HT groups (all P<0.05).

SVS1, SVS3, DVS1, and DVS2 differed between the aICH and sICH subgroups (all P<0.05). No significant differences were observed in other parameters (P>0.05).

mCTA parameters prediction models for HT

In the multivariate logistic regression analysis Model-HT1 for HT prediction, ACC was the independent predictor for HT [odds ratio (OR), 13.924; 95% CI: 1.671–115.991; P<0.05], as well as patients’ age (OR, 1.044; 95% CI: 1.001–1.088; P<0.05), NIHSS at admission (OR, 1.080; 95% CI: 1.003–1.162; P<0.05) and the hyperlipidemia history (OR, 2.405; 95% CI: 0.950–6.084; P<0.05). In Model-HT3, ACC (OR, 9.141;95% CI: 1.149–72.723; P<0.05) and the hyperlipidemia history (OR, 2.754; 95% CI: 1.016–7.461; P<0.05) were the independent influencing factors of predicting HT. mCTA parameters prediction models for HT are shown in Table 2.

Model-HT3 showed an improved prediction efficacy which had an AUC of 0.789, was higher than Model-HT1 which had an AUC of 0.755, and Model-HT2 which had an AUC of 0.769. Though the Delong test did not suggest a statistically significant difference in AUCs between models for predicting HT (both P>0.05) (Figure 3). The AUC, sensitivity, and specificity of the Model-HT are listed in Table 3. The Hosmer and Lemeshow analysis showed a good fit for the Model-HT (all P>0.05, Table 3). The calibration curve, decision curve analysis, and clinical impact curve analysis of Model-HT3 are shown in Figure 3.

Figure 3 The ROC analyzed (A) the prediction efficacy of the Model-HT, the calibration curve (B), decision curve analysis (C), and clinical impact curve analysis (D) of Model-HT3. HT, hemorrhagic transformation; ROC, receiver operating characteristic curve; sICH, symptomatic intracerebral hemorrhage.

mCTA parameters prediction models for sICH

In the Model-sICH2 for sICH prediction, DVS2 was the independent predictor for sICH (OR, 0.1; 95% CI: 0.018–0.567; P<0.05), as well as the diabetes mellitus history (OR, 0.151; 95% CI: 0.029–0.788; P<0.05). In Model-sICH3, DVS2 (OR, 0.1; 95% CI: 0.018–0.567; P<0.05) and the diabetes mellitus history (OR, 0.151; 95% CI: 0.029–0.788; P<0.05) were the independent influencing factors of predicting sICH. mCTA parameters prediction models for sICH are also shown in Table 2.

Model-sICH2 showed an improved prediction efficacy which had an AUC of 0.828, was higher than Model-sICH1 which had an AUC of 0.714, and Model-sICH3 which had an AUC of 0.826. Though the Delong test did not suggest a statistically significant difference in AUCs between models for predicting sICH (both P>0.05) (Figure 4). The AUC, sensitivity, and specificity of the Model-sICH are listed in Table 3. The Hosmer-Lemeshow analysis showed a good fit for the Model-sICH (all P>0.05, Table 3). The calibration curve, decision curve analysis, and clinical impact curve analysis of Model-sICH2 are shown in Figure 4.

Figure 4 The ROC analyzed (A) the prediction efficacy of the Model-sICH, the calibration curve (B), decision curve analysis (C), and clinical impact curve analysis (D) of Model-sICH2. ROC, receiver operating characteristic; sICH, symptomatic intracerebral hemorrhage.

Reproducibility analysis

The reproducibility of all mCTA parameters between two neuroradiologists were good agreement for ACC (κ, 0.816; 95% CI: 0.628–0.959; P<0.001), SVS1 (κ, 0.848; 95% CI: 0.767–0.899; P<0.001), SVS2 (κ, 0.874; 95% CI: 0.829–0.907; P<0.001), SVS3 (κ, 0.924; 95% CI: 0.797–1.000; P<0.001), DVS1 (κ, 0.833; 95% CI: 0.734–0.907; P<0.001), DVS2 (κ, 0.850; 95% CI: 0.737–0.942; P<0.001), DVS3 (κ, 0.905; 95% CI: 0.652–1.000; P=0.005).

Discussion

In this study, we investigated the value of ACC, SVS, and DVS based on mCTA for stratifying the severity of HT and predicting HT and sICH risk in patients with anterior circulation AIS after EVT. ACC was an independent risk factor for predicting HT, and DVS2, which was the score of ICV in the venous phase of mCTA, showed the potential to identify HT patients at high risk of sICH. In addition, Model-HT3 which was based on clinical variables, ACC, SVS, and DVS showed the best prediction performance between the HT and non-HT patients. Model-sICH2 which was based on the clinical variables, SVS, and DVS showed the best prediction performance in the sICH and aICH subgroups analysis. Decision curve analysis and clinical impact curve analysis results further demonstrated that models based on mCTA could provide a high clinical net benefit, which was crucial for assessing the risk of HT and sICH accurately.

Collateral circulation status is a strong predictor of the prognosis for stroke patients (19). mCTA can reflect the ACC at different phases accurately. Previous studies have indicated that good collateral circulation could reduce the risk of HT (12,20,21), and our study supported the idea. Moreover, according to the multivariate model analysis, ACC was a risk factor for predicting HT. This suggested that the degree of hemodynamic disturbances can help identify patients at high risk for HT.

As to the two subgroups of HT, the rate of poor ACC in the aICH subgroup was slightly lower than the sICH subgroup, though there was no statistical difference. This result was inconsistent with the finding by Kuang et al. (20), which indicated that the ACC was different in patients with and without sICH, however, in their study both aICH and non-HT patients were considered patients without sICH. The following factors could explain why there was no statistical difference in ACC between the two subgroups in our study: On the one hand, early restoration of anterograde blood flow preserved substantial ischemic brain tissue after EVT, reversed the pathophysiological process of HT, and may minimize the association between ACC and hemorrhage. On the other hand, the longer ischemia time from symptom onset to CT in patients with sICH compared to the aICH subgroup may cause severe cellular edema and neuronal injury, resulting in the blood-brain barrier destruction severely, thus leading to sICH.

In this study, we adopted an important innovation that combined SVS and DVS based on mCTA to dynamically evaluate venous drainage and predict the risk of HT and sICH after EVT. Compared with prior scoring systems for evaluating superficial veins based on mCTA, such as prognostic evaluation based on cortical vein score difference in stroke score (11) and cortical vein opacification score (9), only three cortical venous opacifications were analyzed. And the total venous score (10), was the sum score of 3 phases of mCTA, which could not reflect each phase of the venous drainage situation. In this study, SVS could detect dynamic changes in all superficial venous drainage from the MCA region in three phases, which enabled a more accurate depiction of venous opacification. On the other hand, previous studies about deep venous drainage focused on the correlation between deep venous asymmetry and poor clinical outcomes (18), rather than HT and sICH. In this study, DVS was developed to assess the deep venous drainage semi-quantitatively and dynamically.

Our results showed that SVS1, SVS2, and SVS3 varied between HT and non-HT groups, indicating that the velocity and volume of the superficial vein drainage in HT patients were significantly lower than non-HT patients, suggesting that veins with poor drainage may aggravate the severity of ischemia, thus further increasing the risk of HT. Our findings were consistent with those by Cao et al. (22), who assessed the predictive value of four superficial veins on CT perfusion for HT after EVT. Their results showed that favorable venous drainage was associated with reduced risk of HT.

Moreover, the results of our study showed there were obvious differences in SVS1, SVS3, DVS1, and DVS2 between the aICH and sICH subgroups, and DVS2 was an independent predictor to predict sICH (OR, 0.1; P<0.05) rather than ACC and other venous parameters. The reasons may be as follows: first, venous drainage reflected both blood flow penetration into and out of the ischemic tissue, it integrated information about the microcirculation status of global tissue, therefore it was conferred to be more sensitive to assessing tissue perfusion (23). Second, DVS drained deeper structures of the brain, and decreasing blood flow to these regions may exacerbate the degree of vascular damage in the ischemic region, increasing the likelihood of sICH. Furthermore, if the draining vein was patent, the contrast agent may be washed out in the late venous phase. This situation may result in underestimation of deep venous drainage in the late venous phase, while the venous phase would not be underestimated (24). Therefore, poor deep venous drainage in the venous phase was an independent predicting factor for sICH. Our findings may provide imaging evidence for identifying patients at high risk of sICH. Neurologists can then implement more stringent blood pressure control targets, conduct more intensive postoperative neurological monitoring, and initiate earlier imaging follow-up for these patients.

The prediction model integrating clinical variables, ACC, SVS, and DVS improved the prediction performance (AUC, 0.789) for HT, indicating that mCTA could evaluate the microcirculation status of the ischemic brain tissue synthetically. Furthermore, the combination of ACC, SVS, DVS, and clinical variables suggested the need for a comprehensive evaluation of radiographic and clinical data in patients with AIS.

There are certain limitations in this study. First, our study was a single-center retrospective cohort analysis. A further large sample multicenter study is necessary to confirm our results. Second, we did not categorize patients by the dominant hemisphere, future studies should include related content to evaluate the profound significance of dominant hemispheric injury on neurological decline. Furthermore, we did not include CT perfusion parameters in the study, and such an analysis may give us a better understanding of the potential effects of cerebral hemodynamics.

Conclusions

In conclusion, ACC, SVS, and DVS based on mCTA are valuable for predicting the risk of HT and sICH after EVT. The combination of multiple parameters can improve the predictive efficacy. We found that ACC was an independent predictor for HT, and poor venous drainage in the venous phase was associated with a high risk of sICH. This study prompts neurologists to perform preventive interventions in high-risk patients after EVT cogently.

Acknowledgments

We want to acknowledge all technicians, the Radiology department of Tianjin Huanhu Hospital, for image acquisition.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2664/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2664/dss

Funding: This work was supported by the Tianjin Health Science and Technology Project (No. TJWJ2022QN061).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2664/coif). L.T.W. is currently employed by GE HealthCare, the manufacturer of the CT system used in this study. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The Institutional Review Board of Tianjin Huanhu Hospital approved the study (Clinical Study Approval 2022-072), and individual informed consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging. N Engl J Med 2018;378:708-18. [Crossref] [PubMed]
2. Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med 2018;378:11-21. [Crossref] [PubMed]
3. Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 2016;387:1723-31. [Crossref] [PubMed]
4. Berger C, Fiorelli M, Steiner T, Schäbitz WR, Bozzao L, Bluhmki E, Hacke W, von Kummer R. Hemorrhagic transformation of ischemic brain tissue: asymptomatic or symptomatic? Stroke 2001;32:1330-5. [Crossref] [PubMed]
5. Menon BK, d'Esterre CD, Qazi EM, Almekhlafi M, Hahn L, Demchuk AM, Goyal M, Multiphase CT. Angiography: A New Tool for the Imaging Triage of Patients with Acute Ischemic Stroke. Radiology 2015;275:510-20. [Crossref] [PubMed]
6. Busto G, Morotti A, Carlesi E, Fiorenza A, Di Pasquale F, Mancini S, Lombardo I, Scola E, Gadda D, Moretti M, Miele V, Fainardi E. Pivotal role of multiphase computed tomography angiography for collateral assessment in patients with acute ischemic stroke. Radiol Med 2023;128:944-59. [Crossref] [PubMed]
7. Wang Z, Xie J, Tang TY, Zeng CH, Zhang Y, Zhao Z, Zhao DL, Geng LY, Deng G, Zhang ZJ, Ju SH, Teng GJ. Collateral Status at Single-Phase and Multiphase CT Angiography versus CT Perfusion for Outcome Prediction in Anterior Circulation Acute Ischemic Stroke. Radiology 2020;296:393-400. [Crossref] [PubMed]
8. Dundamadappa S, Iyer K, Agrawal A, Choi DJ, Multiphase CT. Angiography: A Useful Technique in Acute Stroke Imaging-Collaterals and Beyond. AJNR Am J Neuroradiol 2021;42:221-7. [Crossref] [PubMed]
9. Chu Y, Yin ZX, Ni WJ, Lu SS, Shi HB, Liu S, Wu FY, Xu XQ. Prognostic Value of Venous Outflow Profiles on Multiphase CT Angiography for the Patients with Acute Ischemic Stroke After Endovascular Thrombectomy. Transl Stroke Res 2024;15:1123-32. [Crossref] [PubMed]
10. Singh N, Bala F, Kim BJ, Najm M, Ahn SH, Fainardi E, Rubiera M, Khaw AV, Zini A, Goyal M, Menon BK, Almekhlafi M. Time-resolved assessment of cortical venous drainage on multiphase CT angiography in patients with acute ischemic stroke. Neuroradiology 2022;64:897-903. [Crossref] [PubMed]
11. Drozdov AA, Arora M, Leon Guerrero CR, Sparks AD, Reza Taheri M. Appearance of medullary and cortical veins on multiphase CT-angiography in patients with acute ischemic stroke. Clin Neurol Neurosurg 2023;224:107523. [Crossref] [PubMed]
12. Li X, Liu H, Zeng W, Liu X, Wen Y, Xiong Q, Yang R. The Value of Whole-Brain Perfusion Parameters Combined with Multiphase Computed Tomography Angiography in Predicting Hemorrhagic Transformation in Ischemic Stroke. J Stroke Cerebrovasc Dis 2020;29:104690. [Crossref] [PubMed]
13. Cao R, Ye G, Wang R, Xu L, Jiang Y, Wang G, Wang D, Chen J. Collateral Vessels on 4D CTA as a Predictor of Hemorrhage Transformation After Endovascular Treatments in Patients With Acute Ischemic Stroke: A Single-Center Study. Front Neurol 2020;11:60. [Crossref] [PubMed]
14. Cao R, Jiang Y, Li L, Yang X, Wang H, Chen M, Chen J. Venous collaterals in acute ischemic stroke patients after endovascular treatments: a novel scoring system using 4D computed tomography angiography. Quant Imaging Med Surg 2022;12:5030-43. [Crossref] [PubMed]
15. Larrue V, von Kummer RR, Müller A, Bluhmki E. Risk factors for severe hemorrhagic transformation in ischemic stroke patients treated with recombinant tissue plasminogen activator: a secondary analysis of the European-Australasian Acute Stroke Study (ECASS II). Stroke 2001;32:438-41. [Crossref] [PubMed]
16. Yu X, Pan J, Zhao X, Hou Q, Liu B. Predicting hemorrhagic transformation after thrombectomy in acute ischemic stroke: a multimodal score of the regional pial collateral. Neuroradiology 2022;64:493-502. [Crossref] [PubMed]
17. Tong LS, Guo ZN, Ou YB, Yu YN, Zhang XC, Tang J, Zhang JH, Lou M. Cerebral venous collaterals: A new fort for fighting ischemic stroke? Prog Neurobiol 2018;163-164:172-93. [Crossref] [PubMed]
18. Myint MZ, Yeo LL, Tan BYQ. The EZ, Lim MC, Sia CH, Teoh HL, Sharma VK, Chan B, Ahmad A, Paliwal P, Gopinathan A, Yang C, Makmur A, Andersson T, Arnberg F, Holmin S. Internal cerebral vein asymmetry is an independent predictor of poor functional outcome in endovascular thrombectomy. J Neurointerv Surg 2022;14:683-7. [Crossref] [PubMed]
19. Liu L, Su X, Zhang L, Li Z, Bu K, Yuan S, Wang Q, Wang Y, Aime NJ, Liu Z, Zhou C, Yu J, Tan G, Guo L, Liu X. Cerebral collateral circulation as an independent predictor for in-stent restenosis after carotid artery stenting. Quant Imaging Med Surg 2023;13:2941-52. [Crossref] [PubMed]
20. Kuang Y, Zhang L, Ye K, Jiang Z, Shi C, Luo L. Clinical and imaging predictors for hemorrhagic transformation of acute ischemic stroke after endovascular thrombectomy. J Neuroimaging 2024;34:339-47. [Crossref] [PubMed]
21. Elsaid N, Bigliardi G, Dell'Acqua ML, Vandelli L, Ciolli L, Picchetto L, Borzì G, Ricceri R, Pentore R, Vallone S, Meletti S, Saied A. Proposal of multimodal computed tomography-based scoring system in prediction of hemorrhagic transformation in acute ischemic stroke. Acta Neurol Belg 2023;123:1405-11. [Crossref] [PubMed]
22. Cao R, Ye G, Lu Y, Wang Y, Jiang Y, Sun C, Chen M, Chen J. The Predictive Value of Cerebral Veins on Hemorrhagic Transformation After Endovascular Treatment in Acute Ischemic Stroke Patients: Enhanced Insights From Venous Collateral Circulation Analysis Using Four-Dimensional CTA. Acad Radiol 2024;31:1024-35. [Crossref] [PubMed]
23. Wang X, Lyu J, Duan Q, Li C, Huang J, Meng Z, et al. Deep medullary vein damage correlates with small vessel disease in small vessel occlusion acute ischemic stroke. Eur Radiol 2024;34:6026-35. [Crossref] [PubMed]
24. Peretz S, Pardo K, Naftali J, Findler M, Raphaeli G, Barnea R, Khasminsky V, Auriel E. Delayed CTP-Derived Deep Venous Outflow: A Novel Predictor of Striatocapsular Infarction after M1 Thrombectomy. AJNR Am J Neuroradiol 2022;43:1608-14. [Crossref] [PubMed]