Journal article
Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm
Stroke: vascular and interventional neurology, Vol.5(2), e001509
03/2025
DOI: 10.1161/SVIN.124.001509
PMCID: PMC12671631
PMID: 41573179
Appears in UI Libraries Support Open Access
Abstract
BACKGROUND
A simplified patient selection paradigm with noncontrast computed tomography (NCCT) can reduce the time to reperfusion and widen the eligibility of acute ischemic stroke large vessel occlusions (LVOs) for endovascular therapy. The objectives of this article are (1) to develop, train, and internally validate a deep learning algorithm that estimates baseline infarct core volume (ICV) on NCCT in anterior circulation LVO patients, and (2) by using an external set, to ascertain how this algorithm's (aICV‐NCCT) predictive performance compares with Alberta Stroke Program Early Computed Tomography Score‐NCCT and ICV‐CT perfusion in its capacity to estimate the final infarct volume established on diffusion‐weighted magnetic resonance imaging at 24‐ to 48‐hour follow‐up.
METHODS
In the first phase, stroke activations with baseline NCCT and CT angiography were used to train an aICV‐NCCT. The algorithm was then internally validated using intraclass correlations and Intersection over Union. In the external set, patients with LVO treated with endovascular therapy achieving modified Thrombolysis in Cerebral Infarction score ≥2b and available baseline NCCT, CT angiography, and CT perfusion were included.
RESULTS
A total of 2858 studies of patients with stroke alerts were used for training (80%) and internal validation (20%). We obtained a high correlation (intraclass correlation coefficient, 0.78; CI, 0.73–0.83) and an acceptable Intersection over Union of 0.24 on the internal validation set. The external set consisted on 230 patients with an LVO. When predicting final infarct volume on the external set, our aICV‐NCCT was similar to ICV‐CT perfusion (intraclass correlation coefficient, 0.50 versus 0.54; P = 0.764) and Alberta Stroke Program Early Computed Tomography Score‐NCCT (rs, −0.41; P = 0.436).
CONCLUSION
In this study, we developed and validated a deep learning algorithm that demonstrates an at least equivalent performance to CT perfusion in estimating core volume on acute stroke imaging studies in patients with suspected anterior circulation LVO strokes. The algorithm's robust performance holds significant potential in settings with limited access to advanced imaging technologies across diverse healthcare environments.
Details
- Title: Subtitle
- Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm
- Creators
- Santiago Ortega-Gutierrez - University of Iowa Hospitals and ClinicsJuan Vivanco-Suarez - University of IowaAaron Rodriguez-Calienes - Department of Neurology University of Iowa Hospitals and Clinics Iowa City IAVictor SalviaMilagros Galecio-Castillo - University of IowaMahmoud Dibas - University of IowaYujing Lu - Department of Neurology University of Iowa Hospitals and Clinics Iowa City IAAlba Garcia ReyCristian MartiLeonardo TanziMaria Hernandez-Perez - Hospital Universitari Germans Trias i PujolTudor Jovin - Cooper University HospitalMarc Ribo - Vall d'Hebron Hospital Universitari
- Resource Type
- Journal article
- Publication Details
- Stroke: vascular and interventional neurology, Vol.5(2), e001509
- DOI
- 10.1161/SVIN.124.001509
- PMID
- 41573179
- PMCID
- PMC12671631
- NLM abbreviation
- Stroke Vasc Interv Neurol
- ISSN
- 2694-5746
- eISSN
- 2694-5746
- Publisher
- Wiley
- Grant note
- Methinks
This study was supported in part by Methinks. The funding/sponsor was not directly involved in the following: (1) design and conduct of the study; (2) col-lection, management, analysis and interpretation of data on the external com-parative analysis; (3) preparation, review, or approval of the manuscript; and (4)decision to submit the manuscript for publication
- Language
- English
- Electronic publication date
- 12/19/2024
- Date published
- 03/2025
- Academic Unit
- Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
- Record Identifier
- 9984769723402771
Metrics
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