BMC Medical Imaging investigators implement a radiomics pipeline extracting high-order texture features from NCCT scans, co-registered with diffusion-weighted MRI, to train a random forest classifier that accurately discriminates acute ischemic stroke lesions within six hours, facilitating rapid, accessible early diagnosis.

Key points

• Co-registered NCCT and DWI images from 228 acute ischemic stroke patients enable precise infarct labeling for radiomic analysis.
• Ten RPT-selected radiomic features—including wavelet, LoG, and gradient textures—are normalized and input into a random forest classifier.
• Model achieves AUROCs of 0.858/0.829/0.789 and accuracies up to 79.4%, enabling subvisual infarct detection within six hours on standard CT.

Why it matters: Subvisual stroke lesion detection on routine CT scans expedites early intervention and democratizes acute ischemic stroke diagnosis in resource-limited settings.