Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer

Figure 1 Recruitment and selection process of patients. MRI: Magnetic resonance imaging; PCR: Polymerase chain reaction.

Figure 2 Receiver operating characteristic curve, the area under the receiver operating characteristic curve, and decision curve analysis. A: Receiver operating characteristic (ROC) curve of B7-H3mRNA in the study queue; B: Model performance in the study cohort. the area under the ROC curve (AUC) value increases when radiomic features are combined with B7-H3 mRNA expression compared to different feature combinations. Additionally, it can be observed that as more feature species are used, the AUC value increases, indicating better model performance; C: Decision curve analysis. The y-axis represents net benefit. The threshold probability refers to the point at which the perceived benefit of treating patients with intermediate to high-risk lymph node metastasis is considered equivalent to the harm of overtreating low-risk disease, reflecting how patients weigh the benefits and harms associated with decision-making. The higher curve at any given threshold probability represents the optimal prediction that maximizes net benefit. The decision curve indicates that the combined predictive model used provides greater net benefit compared to other models. AUC: The area under the receiver operating characteristic curve; CT: Computed tomography.

Figure 3 Texture feature selection using the Least Absolute Shrinkage and Selection Operator binary logistic regression model. A: The tuning parameter (Lambda) selection in the Least Absolute Shrinkage and Selection Operator (LASSO) model was performed using 10-fold cross-validation with the minimum criterion. The relationship curve between the mean-square error and Lambda is depicted, with a dashed line indicating the optimal value. The vertical lines represent the values selected through 10-fold cross-validation, including 18 optimized non-zero coefficients; B: LASSO coefficient profiles of 1169 texture features. The coefficient profiles were generated based on the sequence of log (Lambda). When using the value selected by 10-fold cross-validation, the optimal Lambda resulted in 18 non-zero coefficients. MSE: Mean-square error.

Figure 4 Developed radiomics nomogram. The radiomics nomogram was developed with the radiomics signature, B7-H3mRNA level, and computed tomography-reported lymph node status incorporated. CT: Computed tomography.