Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning

Defining the optimal size of an aspiration catheter in relation to the arterial diameter during mechanical thrombectomy for stroke

BACKGROUND

Mechanical thrombectomy for acute ischemic stroke is effective and includes different technical approaches. Operators use direct aspiration, a stent retriever, or a combination of both. Direct aspiration can be performed with various catheters of different sizes depending on the diameter of the occluded vessel.

PURPOSE

We studied the relationship between the catheter diameter in regards to the occluded vessel diameter and the rate of successful recanalization.

MATERIALS AND METHODS

We conducted a retrospective, monocentric study on a series of consecutive patients treated with mechanical thrombectomy. For each procedure, we extracted each attempt that used direct aspiration and rated the attempt as successful or unsuccessful. We also measured the occluded artery diameter and calculated the ratio between the occluded artery and the aspiration catheter diameters. We tested the association between the diameter ratio and the recanalization status. We also performed inter-rater agreement for the arterial diameter measurement between three interventional neuroradiologists.

RESULTS

We included 119 patients with 201 attempts of direct aspiration. A higher diameter ratio was associated with a higher recanalization rate. The analysis in terciles showed that the odds of success were 4.80 higher when the ratio was >0.71 vs <0.54 (p < 0.01). Inter-rater agreement showed near-perfect intraclass correlation with 0.93 (0.91-0.94) consistency and 0.92 (0.90-0.94) absolute agreement.

CONCLUSIONS

We demonstrated an association between higher recanalization and a diameter of ratio >0.71 between the aspiration catheter and the occluded artery. These results could guide intraoperative decisions regarding the appropriate selection of aspiration catheters during mechanical thrombectomy increasing the rate of successful recanalisation. A larger study could provide additional data to further specify the optimal ratio.

Histologically interpretable clot radiomic features predict treatment outcomes of mechanical thrombectomy for ischemic stroke

PURPOSE

Radiomics features (RFs) extracted from CT images may provide valuable information on the biological structure of ischemic stroke blood clots and mechanical thrombectomy outcome. Here, we aimed to identify RFs predictive of thrombectomy outcomes and use clot histomics to explore the biology and structure related to these RFs.

METHODS

We extracted 293 RFs from co-registered non-contrast CT and CTA. RFs predictive of revascularization outcomes defined by first-pass effect (FPE, near to complete clot removal in one thrombectomy pass), were selected. We then trained and cross-validated a balanced logistic regression model fivefold, to assess the RFs in outcome prediction. On a subset of cases, we performed digital histopathology on the clots and computed 227 histomic features from their whole slide images as a means to interpret the biology behind significant RF.

RESULTS

We identified 6 significantly-associated RFs. RFs reflective of continuity in lower intensities, scattered higher intensities, and intensities with abrupt changes in texture were associated with successful revascularization outcome. For FPE prediction, the multi-variate model had high performance, with AUC = 0.832 ± 0.031 and accuracy = 0.760 ± 0.059 in training, and AUC = 0.787 ± 0.115 and accuracy = 0.787 ± 0.127 in cross-validation testing. Each of the 6 RFs was related to clot component organization in terms of red blood cell and fibrin/platelet distribution. Clots with more diversity of components, with varying sizes of red blood cells and fibrin/platelet regions in the section, were associated with RFs predictive of FPE.

CONCLUSION

Upon future validation in larger datasets, clot RFs on CT imaging are potential candidate markers for FPE prediction.

Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion

OBJECTIVE

Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO.

METHODS

All patients who underwent MT for ACLVO between 2015 - 2021 at a single institution were reviewed. Data was divided into 80% training and 20% test sets. 10 models were evaluated on the training set. The top 3 models underwent hyperparameter tuning using grid search with nested 5-fold CV to optimize the area under the receiver operating curve (AUROC). Tuned models were evaluated on the test set and compared to logistic regression.

RESULTS

A total of 381 patients met the inclusion criteria. There were 50 (13.1%) patients who developed MMI. Out of the 10 ML models screened on the training set, the top 3 performing were neural network (median AUROC 0.78, IQR 0.72 - 0.83), support vector machine ([SVM] median AUROC 0.77, IQR 0.72 - 0.83), and random forest (median AUROC 0.75, IQR 0.68 - 0.81). On the test set, random forest (median AUROC 0.78, IQR 0.73 - 0.83) and neural network (median AUROC 0.78, IQR 0.73 - 0.83) were the top performing models, followed by SVM (median AUROC 0.77, IQR 0.70 - 0.83). These scores were significantly better than those for logistic regression (AUROC 0.72, IQR 0.66 - 0.78), individual risk factors, and the Malignant Brain Edema score (p < 0.001 for all).

CONCLUSION

ML models predicted MMI with good discriminative ability. They outperformed standard statistical techniques and individual risk factors.

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

Introduction

Machine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.

Methods

Literature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.

Results

Sixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77-0.85, AUC range: 0.68-0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70-0.81, AUC range: 0.71-0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56-0.88, AUC range: 0.55-0.88) and one DL model (AUC=0.65, 95% CI: 0.62-0.68).

Conclusions

Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524.

Posterior Circulation Endovascular Thrombectomy for Large Vessels Occlusion in Patients Presenting with NIHSS Score ≤ 10

Mechanical thrombectomy (MT) is currently the gold standard treatment for ischemic stroke due to large vessel occlusion (LVO). However, the evidence of clinical usefulness of MT in posterior circulation LVO (pc-LVO) is still doubtful compared to the anterior circulation, especially in patients with mild neurological symptoms. The database of 10 high-volume stroke centers in Europe, including a period of three year and a half, was screened for patients with an acute basilar artery occlusion or a single dominant vertebral artery occlusion ("functional" BAO) presenting with a NIHSS ≤10, and with at least 3 months follow-up. A total of 63 patients were included. Multivariate analysis demonstrated that female gender (adjusted OR 0.04; 95% CI 0-0.84; p = 0.04) and combined technique (adj OR 0.001; 95% CI 0-0.81; p = 0.04) were predictors of worse outcome. Higher pc-ASPECTS (adj OR 4.75; 95% CI 1.33-16.94; p = 0.02) and higher Delta NIHSS (adj OR 2.06; 95% CI 1.16-3.65; p = 0.01) were predictors of better outcome. Delta NIHSS was the main predictor of good outcome at 90 days in patients with posterior circulation LVO presenting with NIHSS score ≤ 10.