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

Yan-Han Xu; Peng Lu; Ming-Cheng Gao; Rui Wang; Yang-Yang Li; Rong-Qi Guo; Wei-Song Zhang; Jian-Xiang Song

doi:10.5306/wjco.v15.i3.419

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

World J Clin Oncol. 2024 Mar 24;15(3):419-433. doi: 10.5306/wjco.v15.i3.419.

Authors

Yan-Han Xu^{1

2}, Peng Lu³, Ming-Cheng Gao^{1

2}, Rui Wang^{1

2}, Yang-Yang Li^{1

2}, Rong-Qi Guo^{1

2}, Wei-Song Zhang^{1

2}, Jian-Xiang Song⁴

Affiliations

¹ School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China.
² Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
³ Department of Imaging, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
⁴ Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China. jxsongycsy@163.com.

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.