CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time

Abstract

Objective: This study was aimed to discuss the application of radiomics using CT analysis in basal ganglia infarction (BGI) for determining the time since stroke onset (TSS) which could provide critical information to clinicians in deciding stroke treatment options such as thrombolysis.

Methods: This study involved 316 patients with BGI (237 in the training cohort and 79 in the independent validation cohort). Region of interest segmentation and feature extraction was done by ITK-SNAP software. We used the existing medical history to binarize the TSS into two categories: positive (< 4.5 h) and negative (≥ 4.5 h). The key radiomic signature features were retrieved by the least absolute shrinkage and selection operator multiple logistic regression model. Receiver operating characteristic curve and AUC analysis were used to evaluate the performance of the radiomic signature in both the training and validation cohorts.

Results: 295 features were extracted from a manually outlined infarction region. Five features were selected to construct the radiomic signature for TSS classification purposes. The performance of the radiomic signature to distinguish between positive and negative in the training cohort was good, with an AUC of 0.982, a sensitivity of 0.929, and a specificity of 0.959. In the validation cohort, the radiomic signature showed an AUC of 0.974, a sensitivity of 0.951, and a specificity of 0.961.

Conclusion: A unique radiomic signature was constructed for use as a diagnostic tool for discriminating the TSS in BGI and may guide decisions to use thrombolysis in patients with unknown times of BGI onset.

Keywords: Artificial intelligence; Basal ganglia; CT; Radiomic; Stroke.