Prediction of acupuncture efficacy in acute ischemic stroke constructing a clinical-radiomics multimodal model

The aim of this study is to construct and validate a prediction model fusing multimodal radiomics and clinical features to evaluate the prognosis of acute ischemic stroke patients treated with acupuncture.

This study retrospectively included 186 patients with acute ischemic stroke who received acupuncture treatment after stroke. The results of Barthel Index scores before and after treatment were used to determine whether acupuncture was effective or ineffective. All patients were randomly assigned to the training dataset (n = 126) or testing dataset (n = 60) on a 7:3 basis. First, collect baseline clinical data and pre-treatment radiomics data of the infarct lesions, subsequently, perform min-max normalization, then screen variables through Pearson correlation analysis and LASSO regression. Constructing clinical models, radiomics models, and combined models, and comparing the performance of Logistic Regression (LR), LightBoost, and K-Nearest Neighbors (KNN) algorithms in each model. Finally, the best model is selected based on the results of the testing dataset.

Four clinical features and eight radiomics features were finally screened to construct the model. Testing dataset results showed limited performance of the clinical model (AUC = 0.689-0.703) versus the radiomics model (AUC = 0.729-0.759), the combined model performed significantly better (KNN algorithm: AUC = 0.889) and its combined discriminative efficacy was outstanding (accuracy = 0.800, sensitivity = 0.914, specificity = 0.640, precision = 0.781).

The combined model integrating clinical and radiomics features can accurately screen the population benefiting from acupuncture treatment, in which the KNN algorithm has the best stability and provides a reliable basis for individualized treatment decisions.