Noninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a...

To develop and validate non-contrast computed tomography (NCCT)-based radiomics method combines machine learning (ML) to investigate invisible microscopic acute ischaemic stroke (AIS) lesions. We retrospectively analyzed 1122 patients from August 2015 to July 2022, whose were later confirmed AIS by diffusion-weighted imaging (DWI). However, receiving a negative result was reported by radiologists according to the NCCT images. Patients in five institutions (n = 592) were combined to generate training and internal validation sets, remaining in three institutions as external validation sets (n = 204, 53 and 273). Through a series of procedures: head alignment, co-registration of NCCT and DWI, the volume of interest delineation and feature extraction. Multiple ML models (random forest, RF; support vector machine, SVM; logistic regression, LR; multilayer perceptron, MLP) were used to discriminate microscopic AIS and non-AIS. Among 1122 patients included (760 men [67.7%]; median [range] age, 64 [21-96] years). After least absolute shrinkage and selection operator (LASSO) algorithm, 44 optimal features were remained. The radiomics combined ML models were yielded similar mean areas under the receiver operating characteristic curve of 0.808 (95% CI 0.754 to 0.861) for RF, 0.802 (95% CI 0.748 to 0.856) for radial basis kernel function-based SVM, 0.792 (95% CI 0.737 to 0.847) for MLP, 0.792 (95% CI 0.736 to 0.848) for Linear-SVM and 0.787 (95% CI 0.730 to 0.844) for LR, respectively. Combining radiomics with ML models can be an efficient, noninvasive, economical, and reliable technique for evaluating invisible microscopic AIS on NCCT and assisting radiologists to make clinical decisions.