Published online Mar 24, 2024. doi: 10.5306/wjco.v15.i3.419
Peer-review started: November 19, 2023
First decision: January 9, 2024
Revised: January 15, 2024
Accepted: February 6, 2024
Article in press: February 6, 2024
Published online: March 24, 2024
Currently, the main treatment method for esophageal cancer is surgical intervention. However, for patients with lymph node metastasis (LNM), further adjuvant chemotherapy and radiotherapy are required to support the surgical treatment. Therefore, preoperative assessment of lymph node (LN) status in esophageal cancer is of paramount importance. Currently, the preoperative diagnosis of LN status in esophageal cancer mainly relies on imaging examinations such as chest computed tomography (CT), which is limited in its diagnostic value and lacks diversity in methodology. To enhance the accurate diagnosis of preoperative LN status in esophageal cancer patients, we intend to design a clinical radiomics nomogram specifically for the diagnosis of LNM in esophageal cancer patients.
By developing a clinical radiomics nomogram, the preoperative LN diagnostic rate in esophageal cancer patients can be improved. This will enable a clear determination of LNM, thereby providing valuable guidance for the formulation of clinical treatment decisions. This approach aligns with the current trend in healthcare, which emphasizes the development of personalized medical treatment plans.
The clinical radiomics nomogram we have designed encompasses imaging radiomic features from chest magnetic resonance imaging (MRI), clinical characteristics of the patients, and the expression level of B7-H3 mRNA obtained through gastric endoscopy. All the indicators in the nomogram can be easily obtained in a clinical setting. In our study, we found that this nomogram significantly improves the diagnostic value of preoperative LN status in esophageal cancer patients compared to traditional imaging examination methods. If applied in a clinical setting in the future, it has the potential to provide valuable guidance for the formulation of clinical treatment decisions.
In our study, we obtained esophageal cancer tissue during gastric endoscopy and used real-time quantitative polymerase chain reaction to amplify and analyze the expression level of B7-H3 mRNA. All patients underwent chest MRI examinations, and Python software packages were used to extract imaging radiomic features. Subsequently, in R language, B7-H3 mRNA, MRI radiomic features, and clinical characteristics of the patients were selected and used to construct the clinical radiomics nomogram. We further analyzed the clinical value of the nomogram using receiver operating characteristic (ROC) and decision curve analysis (DCA) curves. The results showed that the nomogram had a higher diagnostic value for preoperative LN assessment compared to traditional imaging diagnostic methods.
By quantitatively analyzing the expression of B7-H3 mRNA in 32 esophageal cancer tissues, with clinical pathological examination results as the gold standard, we plotted the ROC curve to evaluate the diagnostic value of B7-H3 for LNM in esophageal cancer. The area under the ROC curve (AUC) for B7-H3 in detecting LNM in esophageal cancer was 0.718, with a sensitivity of 73.3% and specificity of 70.6%. The optimal diagnostic threshold for B7-H3 in detecting LNM in esophageal cancer was determined to be 2.56. Using the pathological examination results as the gold standard, we calculated the LN status from CT reports, B7-H3 mRNA expression, MRI radiomic features, and a combined predictive model. Furthermore, we used ROC curves to display the diagnostic performance of CT reports, B7-H3 mRNA expression, MRI radiomic features, and the created combined predictive model for preoperative LN status. Through comparison, we found that the combined predictive model showed the best discriminative ability and predictive stability, with the highest AUC value. Based on the DCA of the combined predictive model, compared to DCA using a single radiomic feature, the addition of B7-H3 mRNA expression and clinical CT results in the combined predictive model demonstrated higher accuracy in predicting preoperative LN status. This suggests that the DCA based on this predictive model is a reliable clinical tool for predicting preoperative LN status in esophageal cancer patients. DCA indicates that the decision curve based on the combined model adds more net benefit to the "treatment" strategy when the threshold probability for patients is within the range of approximately 0.3 to 0.7.
Our study has developed a clinical radiomics nomogram based on multimodal MRI, B7-H3 mRNA expression, and clinical characteristics of patients, which can be applied for preoperative LN status diagnosis in esophageal cancer patients. Compared to conventional imaging examinations, this clinical radiomics nomogram improves the accuracy of preoperative LN status diagnosis. This innovation addresses the challenge of accurately determining LN status before surgery and further facilitates optimal decision-making for the diagnosis and treatment of esophageal cancer patients. The ROC and DCA curves based on this nomogram demonstrate its significant research value in the diagnostic performance of esophageal cancer.
Although the nomogram demonstrates promising diagnostic value and clinical applicability, it is important to acknowledge that the sample size in this study is relatively small. Furthermore, there was a lack of further validation cohorts to validate the nomogram. In the future, a multi-center collaborative study should be conducted to increase the sample size and design validation cohorts to confirm the effectiveness of the nomogram. Additionally, with the rapid development of genomics, integrating genomic data with radiomics may further enhance the clinical decision-making value of the designed nomogram.