Prediction of the trans-stenotic pressure gradient with arteriography-derived hemodynamic features in patients with idiopathic intracranial hypertension

Differentiation Between the Low and High Trans-Stenotic Pressure Gradient in Patients With Idiopathic Intracranial Hypertension Using 4D Flow MRI-Derived Hemodynamic Parameters

BACKGROUND

Trans-stenotic pressure gradient (TPG) measurement is essential for idiopathic intracranial hypertension (IIH) patients with transverse sinus (TS) stenosis. Four-D flow MRI may provide a noninvasive imaging method for differentiation of IIH patients with different TPG.

PURPOSE

To investigate the associations between 4D flow parameters and TPG, and to evaluate the diagnostic performance of 4D flow parameters in differentiating patients with high TPG (GroupHP) from low TPG (GroupLP).

STUDY TYPE

Prospective.

POPULATION

31 IIH patients with TS stenosis (age, 38 ± 12 years; 23 females) and 5 healthy volunteers (age, 25 ± 1 years; 2 females).

FIELD STRENGTH/SEQUENCE

3T, 3D phase contrast MR venography, and gradient recalled echo 4D flow sequences.

ASSESSMENT

Scan-rescan reproducibility of 4D flow parameters were performed. The correlation between TPG and flow parameters was analyzed. The netflow and velocity difference between inflow plane, outflow plane, and the stenosis plane were calculated and compared between GroupHP and GroupLP.

STATISTICAL TESTS

Pearson's correlation or Spearman's rank correlation coefficient, Independent samples t-test or Wilcoxon rank-sum test, Intra-class correlation coefficient (ICC), Bland-Altman analyses, Receiver operating characteristic curves. A P value <0.05 was considered significant.

RESULTS

Significant correlations were found between TPG and netflow parameters including Favg,out-s, Favg,in-s, Fmax,out-s, and Fmax,in-s (r = 0.525-0.565). Significant differences were found in Favg,out-s, Fmax,out-s, Favg,in-s, and Fmax,in-s between GroupHP and GroupLP. Using the cut-off value of 2.19 mL/sec, the Favg,out-s showed good estimate performance in distinguishing GroupHP from GroupLP (AUC = 0.856). The ICC (ranged 0.905-0.948) and Bland-Altman plots indicated good scan-rescan reproducibility.

DATA CONCLUSIONS

4D flow MRI derived flow parameters showed good correlations with TPG in IIH patients with TS stenosis. Netflow difference between outflow and stenosis location at TS shows the good performance in differentiating GroupHP and GroupLP cases.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage

BACKGROUND

Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage.

METHODS

This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets.

RESULTS

A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively.

CONCLUSIONS

The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization

Background and purpose

Postinterventional rupture of intracranial aneurysms (IAs) remains a severe complication after flow diverter treatment. However, potential hemodynamic mechanisms underlying independent predictors for postinterventional rupture of IAs remain unclear. In this study, we employed arteriography-derived radiomic features to predict this complication.

Methods

We included 64 patients who underwent pipeline flow diversion for intracranial aneurysms, distinguishing between 16 patients who experienced postinterventional rupture and 48 who did not. We performed propensity score matching based on clinical and morphological factors to match these patients with 48 patients with postinterventional unruptured IAs at a 1:3 ratio. Postinterventional digital subtraction angiography were used to create five arteriography-derived perfusion parameter maps and then radiomics features were obtained from each map. Informative features were selected through the least absolute shrinkage and selection operator method with five-fold cross-validation. Subsequently, radiomics scores were formulated to predict the occurrence of postinterventional IA ruptures. Prediction performance was evaluated with the training and test datasets using area under the curve (AUC) and confusion matrix-derived metrics.

Results

Overall, 1,459 radiomics features were obtained, and six were selected. The resulting radiomics scores had high efficacy in distinguishing the postinterventional rupture group. The AUC and Youden index were 0.912 (95% confidence interval: 0.767-1.000) and 0.847 for the training dataset, respectively, and 0.938 (95% confidence interval, 0.806-1.000) and 0.800 for the testing dataset, respectively.

Conclusion

Radiomics scores generated using arteriography-derived radiomic features effectively predicted postinterventional IA ruptures and may aid in differentiating IAs at high risk of postinterventional rupture.

Prediction of Venous Trans-Stenotic Pressure Gradient Using Shape Features Derived From Magnetic Resonance Venography in Idiopathic Intracranial Hypertension Patients

OBJECTIVE

Idiopathic intracranial hypertension (IIH) is a condition of unknown etiology associated with venous sinus stenosis. This study aimed to develop a magnetic resonance venography (MRV)-based radiomics model for predicting a high trans-stenotic pressure gradient (TPG) in IIH patients diagnosed with venous sinus stenosis.

MATERIALS AND METHODS

This retrospective study included 105 IIH patients (median age [interquartile range], 35 years [27-42 years]; female:male, 82:23) who underwent MRV and catheter venography complemented by venous manometry. Contrast enhanced-MRV was conducted under 1.5 Tesla system, and the images were reconstructed using a standard algorithm. Shape features were derived from MRV images via the PyRadiomics package and selected by utilizing the least absolute shrinkage and selection operator (LASSO) method. A radiomics score for predicting high TPG (≥ 8 mmHg) in IIH patients was formulated using multivariable logistic regression; its discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC). A nomogram was constructed by incorporating the radiomics scores and clinical features.

RESULTS

Data from 105 patients were randomly divided into two distinct datasets for model training (n = 73; 50 and 23 with and without high TPG, respectively) and testing (n = 32; 22 and 10 with and without high TPG, respectively). Three informative shape features were identified in the training datasets: least axis length, sphericity, and maximum three-dimensional diameter. The radiomics score for predicting high TPG in IIH patients demonstrated an AUROC of 0.906 (95% confidence interval, 0.836-0.976) in the training dataset and 0.877 (95% confidence interval, 0.755-0.999) in the test dataset. The nomogram showed good calibration.

CONCLUSION

Our study presents the feasibility of a novel model for predicting high TPG in IIH patients using radiomics analysis of noninvasive MRV-based shape features. This information may aid clinicians in identifying patients who may benefit from stenting.

High-resolution magnetic resonance imaging-based radiomic features aid in selecting endovascular candidates among patients with cerebral venous sinus thrombosis

OBJECTIVES

Cerebral venous sinus thrombosis (CVST) can cause sinus obstruction and stenosis, with potentially fatal consequences. High-resolution magnetic resonance imaging (HRMRI) can diagnose CVST qualitatively, although quantitative screening methods are lacking for patients refractory to anticoagulation therapy and who may benefit from endovascular treatment (EVT). Thus, in this study, we used radiomic features (RFs) extracted from HRMRI to build machine learning models to predict response to drug therapy and determine the appropriateness of EVT.

MATERIALS AND METHODS

RFs were extracted from three-dimensional T1-weighted motion-sensitized driven equilibrium (MSDE), T2-weighted MSDE, T1-contrast, and T1-contrast MSDE sequences to build radiomic signatures and support vector machine (SVM) models for predicting the efficacy of standard drug therapy and the necessity of EVT.

RESULTS

We retrospectively included 53 patients with CVST in a prospective cohort study, among whom 14 underwent EVT after standard drug therapy failed. Thirteen RFs were selected to construct the RF signature and CVST-SVM models. In the validation dataset, the sensitivity, specificity, and area under the curve performance for the RF signature model were 0.833, 0.937, and 0.977, respectively. The radiomic score was correlated with days from symptom onset, history of dyslipidemia, smoking, fibrin degradation product, and D-dimer levels. The sensitivity, specificity, and area under the curve for the CVST-SVM model in the validation set were 0.917, 0.969, and 0.992, respectively.

CONCLUSIONS

The CVST-SVM model trained with RFs extracted from HRMRI outperformed the RF signature model and could aid physicians in predicting patient responses to drug treatment and identifying those who may require EVT.

Group-based trajectory modeling of intracranial pressure in patients with acute brain injury: Results from multi-center ICUs, 2008-2019

Objective

The objective of the study was to characterize the longitudinal, dynamic intracranial pressure (ICP) trajectory in acute brain injury (ABI) patients admitted to intensive care unit (ICU) and explore whether it added sights over traditional thresholds in predicting outcomes.

Methods

ABI patients with ICP monitoring were identified from two public databases named Medical Information Mart for the Intensive Care (MIMIC)‐IV and eICU Collaborative Research Database (eICU‐CRD). Group‐based trajectory modeling (GBTM) was employed to identify 4‐h ICP trajectories in days 0–5 post‐ICU admission. Then, logistic regression was used to compare clinical outcomes across distinct groups. To further validate previously reported thresholds, we created the receiver operating characteristic (ROC) curve in our dataset.

Results

A total of 810 eligible patients were ultimately enrolled in the study. GBTM analyses generated 6 distinct ICP trajectories, differing in the initial ICP, evolution pattern, and number/proportion of spikes >20/22 mmHg. Compared with patients in “the highest, declined then rose” trajectory, those belonging to the “lowest, stable,” “low, stable,” and “medium, stable” ICP trajectories were at lower risks of 30‐day mortality (odds ratio [OR] 0.04; 95% confidence interval [CI] 0.01, 0.21), (OR 0.04; 95% CI 0.01, 0.19), (OR 0.08; 95% CI 0.01, 0.42), respectively. ROC analysis demonstrated an unfavorable result, for example, 30‐day mortality in total cohort: an area under the curve (AUC): 0.528, sensitivity: 0.11, and specificity: 0.94.

Conclusions

This study identified three ICP trajectories associated with elevated risk, three with reduced risks for mortality during ICU hospitalization. Notably, a fixed ICP threshold should not be applied to all kinds of patients. GBTM, a granular method for describing ICP evolution and their association with clinical outcomes, may add to the current knowledge in intracranial hypertension treatment.