Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia

Figure 2 Selection of radiomic features using the least absolute shrinkage and selection operator regression model. A: Diffusion maps displaying patient clusters based on radiomic features in training set; B: Least absolute shrinkage and selection operator (LASSO) coefficient profile plot showing various log (λ) values; The vertical dashed lines indicate the 13 radiomic features with nonzero coefficients selected using the optimal λ value; C: Selection of the LASSO model’s tuning parameter (λ) utilizing 10-fold cross-test via the minimum criterion, with vertical lines indicating the optimal λ value. 10-fold cross-test was employed for parameter tuning; D: Distribution of the 13 radiomic features with nonzero coefficients. R: Bowel resection group; NR: Non-bowel resection group; MSE: Mean squared error; PC: Principal component; LASSO: Least absolute shrinkage and selection operator.