A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography.
Authors
Affiliations (4)
Affiliations (4)
- Computer Science, Graduate Studies, University of California, 1 Shields Ave, Davis, CA, 95616, USA.
- Department of Radiology, Shandong Provincial Hospital, Shandong First Medical University, Jingwu Road No. 324, Jinan, Shandong, 250021, China.
- Department of Radiology, Children's Hospital Affiliated to Shandong University, Jingshi Road No. 23976, Jinan, Shandong, 250022, China.
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jiyan Road No. 440, Jinan, Shandong, 250117, China. [email protected].
Abstract
In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infarct and the surrounding normal brain tissue on NCCT images within the initial 6 h post-onset, it poses significant challenges in promptly and accurately positioning and quantifying the infarct at the early stage. To investigate whether a radiomics-based model using NCCT could effectively assess the risk of acute ischemic stroke (AIS). This study proposed a machine learning (ML) for infarct detection, enabling automated quantitative assessment of AIS lesions on NCCT images. In this retrospective study, NCCT images from 228 patients with AIS (< 6 h from onset) were included, and paired with MRI-diffusion-weighted imaging (DWI) images (attained within 1 to 7 days of onset). NCCT and DWI images were co-registered using the Elastix toolbox. The internal dataset (153 AIS patients) included 179 AIS VOIs and 153 non-AIS VOIs as the training and validation groups. Subsequent cases (75 patients) after 2021 served as the independent test set, comprising 94 AIS VOIs and 75 non-AIS VOIs. The random forest (RF) model demonstrated robust diagnostic performance across the training, validation, and independent test sets. The areas under the receiver operating characteristic (ROC) curves were 0.858 (95% CI: 0.808-0.908), 0.829 (95% CI: 0.748-0.910), and 0.789 (95% CI: 0.717-0.860), respectively. Accuracies were 79.399%, 77.778%, and 73.965%, while sensitivities were 81.679%, 77.083%, and 68.085%. Specificities were 76.471%, 78.431%, and 81.333%, respectively. NCCT-based radiomics combined with a machine learning model could discriminate between AIS and non-AIS patients within less than 6 h of onset. This approach holds promise for improving early stroke diagnosis and patient outcomes. Not applicable.