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

Abstract

Background: Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis.

Aim: To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients.

Methods: A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model.

Results: The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice.

Conclusion: This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.

Keywords: B7-H3mRNA; Esophageal cancer; Lymph node metastasis; Multimodal magnetic resonance imaging; Nomogram; Radiomics.

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.