Adriaan C. Van Es (Netherlands)
Leiden University Medical Center RadiologyAuthor Of 2 Presentations
BEYOND ETICI 2B REPERFUSION: VALUE OF ADDITIONAL PASSES TO ACHIEVE COMPLETE REPERFUSION
Abstract
Background and Aims
Currently, it is unclear whether during endovascular treatment (EVT) for acute ischemic stroke, an extra pass should be undertaken to achieve more complete reperfusion after expanded Treatment In Cerebral Ischemia (eTICI) 2B is already achieved. We aimed to compare outcomes of single-pass good reperfusion (eTICI 2B) with multi-pass (near-)complete reperfusion (eTICI 2C-3) in daily clinical practice.
Methods
We included MR CLEAN Registry patients with M1 occlusions in whom EVT was ended either after achieving eTICI 2B in a single pass or after achieving eTICI 2C/3 in multiple passes. Regression models were used to investigate the association between single-pass eTICI 2B versus multi-pass eTICI 2C/3 with 24-hour National Institutes of Health Stroke Scale (NIHSS) score and 90-day functional outcome (modified Rankin Scale [mRS]).
Results
In 114 (28%) patients, eTICI 2B was achieved after a single pass; in 292 (72%) patients eTICI2C/3 was achieved after multiple passes. Patients with single-pass eTICI 2B showed lower 24-hour NIHSS scores (-19% [95% CI -33 to -1%]) and better functional outcomes (acOR 1.32 [95 % CI 0.93-1.87]) than patients with eTICI 2C/3 after ≥3 passes (Figure 1). No significant difference in functional outcomes was found between single-pass eTICI 2B and eTICI 2C/3 in two passes.
Conclusions
Our results do not provide arguments to continue an EVT procedure when eTICI 2B is reached after one pass, but further research is necessary to investigate the per-pass effect in relation to reperfusion and functional outcome.
DEEP LEARNING-BASED INTRACRANIAL PERFORATION DETECTION IN DSA IMAGES OBTAINED DURING ENDOVASCULAR THROMBECTOMY
Abstract
Background and Aims
Intracranial vessel perforation is a procedural complication during endovascular thrombectomy (EVT). Its occurrence is strongly associated with unfavorable treatment outcomes. Early identification of perforation would allow therapeutic actions to prevent situation deterioration. Due to its low occurrence and the large heterogeneity in image appearance, perforations may initially be missed by the interventionalists. In this work, we study the feasibility of automated intracranial vessel perforation detection during EVT using deep learning techniques.
Methods
From the MR CLEAN registry, fifty-three patients (149 acquisitions) with vessel perforations were identified and annotated by an experienced neuroradiologist. Another 150 acquisitions (from 150 patients) without perforations were randomly selected as negative samples. The proposed solution builds on top of state-of-the-art object detection algorithms. It incorporates temporal information of DSA with bidirectional convolutional gated recurrent units (Bi-ConvGRU), further followed by a problem-tailored acquisition level optimization to reduce false positives based on temporal consistency.
Results
In ten-fold cross-validation on 203 patients (299 acquisitions, 3607 images), the proposed method achieves an area under the receiver-operating characteristic curve of 90% for acquisition-level classification of acquisitions with a perforation. The sensitivity and specificity were 81% and 86%, respectively.
Conclusions
The proposed deep learning-based algorithm achieves promising performance in vessel perforation detection in DSA for stroke patients, and can potentially be deployed in clinical practice to detect perforation early and allow direct clinical decision making.