Enhancing lymph node metastasis prediction in adenocarcinoma of the esophagogastric junction: A study combining radiomic with clinical features

• Published in: Medical physics (Lancaster) 2024-12, Vol.51 (12), p.9057-9070
• Main Authors: Zheng, Hui‐da, Tian, Yu‐chi, Huang, Qiao‐yi, Huang, Qi‐ming, Ke, Xiao‐ting, Xu, Jian‐hua, Liang, Xiao‐yun, Lin, Shu, Ye, Kai
• Format: Article
• Language: English
• Subjects: Adenocarcinoma - diagnostic imaging; Adenocarcinoma - pathology; adenocarcinoma of the esophagogastric junction; Adult; Aged; computed tomography; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - pathology; Esophagogastric Junction - diagnostic imaging; Esophagogastric Junction - pathology; Female; gastric cancer; Humans; Image Processing, Computer-Assisted - methods; lymph node metastasis; Lymphatic Metastasis - diagnostic imaging; Male; Middle Aged; Nomograms; Radiomics; Retrospective Studies; Stomach Neoplasms - diagnostic imaging; Stomach Neoplasms - pathology; Tomography, X-Ray Computed
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.17374

Background The incidence of adenocarcinoma of the esophagogastric junction (AEJ) is increasing, and with poor prognosis. Lymph node status (LNs) is particularly important for planning treatment and evaluating the prognosis of patients with AEJ. However, the use of radiomic based on enhanced computed tomography (CT) to predict the preoperative lymph node metastasis (PLNM) status of the AEJ has yet to be reported. Purpose We sought to investigate the value of radiomic features based on enhanced CT in the accurate prediction of PLNM in patients with AEJ. Methods Clinical features and enhanced CT data of 235 patients with AEJ from October 2017 to May 2023 were retrospectively analyzed. The data were randomly assigned to the training cohort (n = 164) or the external testing cohort (n = 71) at a ratio of 7:3. A CT‐report model, clinical model, radiomic model, and radiomic‐clinical combined model were developed to predict PLNM in patients with AEJ. Univariate and multivariate logistic regression were used to screen for independent clinical risk factors. Least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomic features. Finally, a nomogram for the preoperative prediction of PLNM in AEJ was constructed by combining Radiomics‐score and clinical risk factors. The models were evaluated by area under the receiver operating characteristic curve (AUC‐ROC), calibration curve, and decision curve analyses. Results A total of 181 patients (181/235, 77.02%) had LNM. In the testing cohort, the AUC of the radiomic‐clinical model was 0.863 [95% confidence interval (CI) = 0.738–0.957], and the radiomic model (0.816; 95% CI = 0.681–0.929), clinical model (0.792; 95% CI = 0.677–0.888), and CT‐report model (0.755; 95% CI = 0.647–0.840). Conclusion The radiomic‐clinical model is a feasible method for predicting PLNM in patients with AEJ, helping to guide clinical decision‐making and personalized treatment planning.