Evaluating the predictive efficacy of multi-parameter MRI based radiomic models on clinical symptom progression in perforator artery cerebral infarction

To assess the predictive efficacy of MRI-based radiomics model on the progressive deterioration of neurological function in patients with subacute ischemic perforating artery cerebral infarction through extracting radiomic features within the infarction area on the images of echo planar imaging diffusion weighted imaging (SEPI-DWI), apparent diffusion coefficient (ADC), and T2-weighted image (T2WI) sequences. This retrospective study included 188 patients with subacute ischemic perforating artery cerebral infarction. The clinical and head MRI imaging data were collected. We randomly divided patients into training and validation cohort in ratio of 7:3. We used independent sample t test, recursive feature elimination combined with least absolute shrinkage and selection operator (LASSO) regression methods to screen features, four prediction models were developed, and their predictive efficiency was evaluated through ROC curve and decision curve analysis (DCA). In combined model, the specificity, sensitivity, accuracy, AUC in training cohort were 0.753, 0.761, 0.756, 0.844, respectively, in validation cohort were 0.730, 0.650, 0.702, 0.824, all the values were higher than those of T2WI-model, SEPI-DWI-model and ADC-model, and DCA curve indicated better predictive efficacy in the specific threshold probability range of the combined model. The MRI-based radiomics model has good predictive efficacy on predicting the progressive neurological dysfunction of patients, and has a certain guiding role in the formulation of personalized treatment for patients with subacute ischemic perforating artery cerebral infarction.