Energy spectrum CT index-based machine learning model predicts the effect of intravenous thrombolysis in lower limbs.

ABSTRACT: To develop a noninvasive machine learning (ML) model based on energy spectrum computed tomography venography (CTV) indices for preoperatively predicting the effect of intravenous thrombolytic treatment in lower limbs. A total of 3492 slices containing thrombus regions from 58 veins in lower limbs in a cohort of 18 patients, divided in good and poor thrombolysis prognosis groups, were analyzed. Key indices were selected by univariate analysis and Pearson correlation coefficient test. A support vector machine classifier-based model was developed through ten-fold cross validation. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness at both per-slice and per-vessel levels. Continuous variables and categorical variables were compared between good and

SUBMITTER: Liu R 

PROVIDER: S-EPMC10338802 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature