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

Figure 1 Workflow of the radiomic analysis. CT: Computed tomography; 3D: Three-dimensional; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver operating characteristic; AUC: Area under the curve; DCA: Decision curve analysis.

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.

Figure 3 A nomogram based on Rad-score and clinical indicators for predicting bowel resection risk in patients with incarcerated inguinal hernia. A and B: Heatmaps illustrating the distribution of the 13 selected radiomic features in both cohorts; C and D: Box plots of the most significantly different radiomic features in the training and test cohorts; E: Construction of the radiomic-clinical nomogram; F and G: Calibration curves for the radiomic nomogram in the training and test sets. R: Bowel resection group; NR: Non-bowel resection group.

Figure 4 Receiver operating characteristic curves and decision curve analysis. A and B: Receiver operating characteristic curves for the clinical factor model, the radiomic signature, and the radiomic nomogram in the training sets (A) and test sets (B); C and D: Decision curve analysis for the radiomic nomogram model in the training (C) and test (D) sets. AUC: Area under the curve; CI: Confidence interval; DCA: Decision curve analysis.

Figure 5 Demonstrations of bowel resection results predicted by the novel fusion model. A-C: Case 1: A 63-year-old male patient diagnosed with incarcerated inguinal hernia (IIH), including the patient’s nomogram, preoperative computed tomography (CT) images, and intraoperative findings of the incarcerated bowel; D-F: Case 2: A 72-year-old male patient diagnosed with IIH, featuring the patient’s nomogram, preoperative CT images, and intraoperative findings from laparoscopic mesh repair procedures for the patient without bowel resection.