Multi-regional feature integration on enhanced CT for lymph node metastasis prediction in gastric cancer: a novel radiomics approach.

BACKGROUND

Lymph node metastasis (LNM) represents the predominant metastatic pathway and a critical prognostic determinant in gastric adenocarcinoma. Accurate preoperative prediction of LNM status is imperative for optimizing tumor staging, therapeutic decision-making, and prognostic evaluation. This study aims to develop and validate a radiomics model utilizing contrast-enhanced computed tomography (CT) features from both tumor and stomach regions for preoperative assessment of LNM status. MATERIALS AND

METHODS

A retrospective analysis was performed on 279 patients who underwent radical gastrectomy, randomly divided into a training cohort (n = 195) and a validation cohort (n = 84). Preoperative contrast-enhanced abdominal CT images were collected, and radiomics features were extracted from both the tumor and stomach. After Z-score normalization, feature selection was conducted using inter- and intra-class correlation, univariate analysis, and LASSO regression. Six machine learning algorithms were used to construct radiomics models based on tumor, stomach, and their combination. Each model was trained using five-fold cross-validation, and their performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Univariate analysis and logistic regression were performed to identify significant clinical features, leading to the development of a combined model incorporating both clinical and radiomics features.

RESULTS

A total of 12 radiomics features were selected to construct the tumor - stomach wall radiomics model. Among the six algorithms, the LightGBM-based model demonstrated superior performance, achieving an AUC of 0.899 (95% CI, 0.858–0.941) in the training set and 0.740 (95% CI, 0.633–0.847) in the validation set. Anemia and abnormal CA199 levels were identified as independent risk factors for LN metastasis in GC. After integrating clinical and radiomics features, the combined model achieved an AUC of 0.903 (95% CI, 0.863–0.944) in the training set and 0.767 (95% CI, 0.664–0.869) in the validation set. Decision curve analysis demonstrated that the combined model had favorable clinical utility.

CONCLUSION

As a non-invasive preoperative predictive tool, the combined model incorporating tumor and stomach radiomics features along with clinical factors shows promising clinical value in assessing LN status in GC patients.