Key result
NCCT radiomics model identifies acute ischemic stroke within 6 hours with ~0.79 AUC.
Why the study?
Does a radiomics-based machine learning model using NCCT effectively detect acute ischemic stroke within 6 hours of onset?
Observational (n=228)
Does a radiomics-based machine learning model using NCCT effectively detect acute ischemic stroke within 6 hours of onset?
A radiomics-based machine learning model using non-contrast CT can help discriminate between acute ischemic stroke and non-stroke tissue within 6 hours of onset, potentially improving early diagnosis.
BACKGROUND: 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. AIMS: To investigate whether a radiomics-based model using NCCT could effectively assess the risk of acute ischemic stroke (AIS). METHODS: 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. RESULTS: 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. CONCLUSION: 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. CLINICAL TRIAL NUMBER: Not applicable.
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Tan et al. (2025) conducted an observational in Acute ischemic stroke (n=228). Radiomics-based machine learning model using NCCT vs. Non-AIS VOIs was evaluated on Discrimination between AIS and non-AIS (AUC on independent test set) (AUC 0.789, 95% CI 0.717-0.860). A radiomics-based random forest model using non-contrast computed tomography discriminated acute ischemic stroke from non-stroke within 6 hours of onset with an AUC of 0.789 (95% CI 0.717-0.860).
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