Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 h: a comparison of approaches.
Authors
Affiliations (10)
Affiliations (10)
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands. [email protected].
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands. [email protected].
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands.
- Informatics Institute, University of Amsterdam, Science Park 900, 1098 XH, Amsterdam, The Netherlands.
- Department of Neurology, Maastricht UMC+, 6229 HX, Maastricht, The Netherlands.
- School for Cardiovascular Diseases (CARIM), Maastricht University, 6229 ER, Maastricht, The Netherlands.
- Department of Radiology and Nuclear Medicine, Maastricht UMC+, 6229 HX, Maastricht, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, 3015 GD, Rotterdam, The Netherlands.
- Department of Neurology, Amsterdam UMC, location University of Amsterdam, 1105 AZ, Amsterdam, The Netherlands.
Abstract
To compare machine learning models using different combinations of clinical and imaging variables for classifying ischemic stroke patients as having an onset-to-imaging (OTI) time within or beyond 4.5 h. We analyzed 993 patients with known OTI time from the MR CLEAN Registry and LATE trial. Data were split into training and test sets (80:20). We developed models using various combinations of variables to classify OTI time, including clinical-radiological information, and variables automatically extracted from segmented ischemic regions on non-contrast CT, such as net water uptake (NWU), lesion volume, and radiomics features. Performance was assessed using the area under the receiver operating characteristic curve (AUC). Of 993 patients, 199 (20%) presented beyond 4.5 h. The model including only clinical-radiological scores, and the one including only NWU, achieved an AUC of 0.65. Performance was higher for models that included NWU combined with lesion volume or clinical-radiological scores (AUCs ranging from 0.70 to 0.75). Radiomics-based models achieved the highest performance with AUCs of 0.81, significantly outperforming NWU-based models. Key predictors for identifying patients beyond 4.5 h included homogeneous lesion textures in both core and hypoperfused areas, smaller hypoperfused area volumes, higher core NWU, and lower baseline NIHSS scores. We found that radiomics-based models outperform models including NWU measurements for classifying stroke OTI time in this endovascular therapy population. The superior performance suggests that texture, shape, and intensity patterns of ischemic lesions may capture more information about lesion age than single metrics like NWU. External validation in broader stroke populations is needed to establish clinical utility. Question Which combinations of clinical and CT-derived variables enable the most accurate classification of stroke onset time within versus beyond 4.5 h using machine learning? Findings Models using radiomics features achieved superior accuracy (AUC 0.81) compared to models using net water uptake measurements (AUC 0.65) for onset time classification. Clinical relevance Automated CT-based radiomics models enable accurate stroke onset time classification without advanced imaging, potentially expanding treatment options for patients with unknown symptom onset times in centers lacking MRI capabilities.