Preoperative prediction of lymph node metastasis in adenocarcinoma of esophagogastric junction using CT texture analysis combined with machine learning.

This study aims to construct a noninvasive preoperative prediction model for lymph node metastasis in adenocarcinoma of esophagogastric junction (AEG) using computed tomography (CT) texture characterization and machine learning. We analyzed clinical and imaging data from 57 patients with preoperative CT enhancement scans and pathologically confirmed AEG. Lesions were delineated, and texture features were extracted from arterial phase and venous phase CT images using 3D-Slicer software. Features were normalized, downscaled, and screened using correlation analysis and the least absolute shrinkage and selection operator algorithm. The lymph node metastasis prediction model employed machine learning algorithms (random forest, logistic regression, decision tree [DT], and support vector machine), with performance validated using receiver operating characteristic curves. In the arterial phase, the random forest model excelled in precision (0.86) and positive predictive value (0.86). The DT model exhibited the best negative predictive value (0.86), while the logistic regression model demonstrated the highest area under the curve (AUC; 0.78) and specificity (1.0). During the venous phase, the DT model excelled in precision (0.72), F1 score (0.76), and recall (0.80), whereas the support vector machine model had the highest AUC (0.75). Differences in AUCs between models in both phases were not statistically significant per DeLong's test, indicating comparable performance. Each model displayed strengths across various metrics, with the DT model showing consistent performance across arterial and venous phases, emphasizing accuracy and specificity. The CT texture-based machine learning model effectively predicts lymph node metastasis noninvasively in AEG patients, demonstrating robust predictive efficacy.