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

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 poor thrombolysis prognosis group by Mann-Whitney U-test and chi-square test, respectively. A nomogram was built by integrating clinical factors and the energy spectrum CTV index-based score calculated by the model. Six indices selected from 192 indices were used to build the predictive model. The ML model achieved area under the curves (AUCs) of 0.838 and 0.767 [95% CI (confidence interval), 0.825-0.850, 0.752-0.781] in the training and validation datasets at the per-slice level, and the per-vessel level AUCs were 0.945 and 0.876 (95% CI, 0.852-0.988, 0.763-0.948) in the training and validation datasets, respectively. The nomogram showed better performance with the per-vessel level AUC, accuracy, sensitivity and specificity, yielding 0.901(95% CI, 0.793-0.964), 86.2%, 87.9% and 84.0% in the validation dataset, respectively. There was no significant difference in the vessel distribution between good and poor thrombolysis prognosis groups (chi-square test, p = 0.671). The energy spectrum CTV index-based ML model achieved favorable effectiveness in predicting the outcome of vessel-level intravenous thrombolysis. A nomogram integrating clinical factors, and risk score calculated by the developed model showed improved performance and had potential to be used as a noninvasive preoperative tool for clinicians.