Noninvasive model for predicting future ischemic strokes in patients with silent lacunar infarction using radiomics.

ABSTRACT:

Background

This study aimed to investigate integrating radiomics with clinical factors in cranial computed tomography (CT) to predict ischemic strokes in patients with silent lacunar infarction (SLI).

Methods

Radiomic features were extracted from baseline cranial CT images of patients with SLI. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was used to select significant prognostic factors based on Model^C with clinical factors, Model^R with radiomic features, and Model^CR with both factors. The Kaplan-Meier method was used to compare stroke-free survival probabilities. A nomogram and a calibration curve were used for further evaluation.

Results

Radiomic signature (p < 0.01), age (p = 0.09), dyslipidemia

SUBMITTER: Su JH 

PROVIDER: S-EPMC7346609 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature