Predictive value of enhanced CT and pathological indicators in lymph node metastasis in patients with gastric cancer based on GEE model.

Objectives: A predictive model was developed based on enhanced computed tomography (CT), laboratory test results, and pathological indicators to achieve the convenient and effective prediction of single lymph node metastasis (LNM) in gastric cancer.

Methods: Sixty-six consecutive patients (235 regional lymph nodes) with pathologically confirmed gastric cancer who underwent surgery at our hospital between December 2020 and November 2021 were retrospectively reviewed. They were randomly allocated to training (n = 38, number of lymph nodes = 119) and validation (n = 28, number of lymph nodes = 116) datasets. The clinical data, laboratory test results, enhanced CT characteristics, and pathological indicators from gastroscopy-guided needle biopsies were obtained. Multivariable logistic regression with generalised estimation equations (GEEs) was used to develop a predictive model for LNM in gastric cancer. The predictive performance of the model developed using the training and validation datasets was validated using receiver operating characteristic curves.

Results: Lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter were independent predictors of LNM in gastric cancer (p < 0.01). The GEE-logistic model was associated with LNM (p = 0.001). The area under the curve and accuracy of the model, with 95% confidence intervals, were 0.944 (0.890-0.998) and 0.897 (0.813-0.952), respectively, in the training dataset and 0.836 (0.751-0.921) and 0.798 (0.699-0.876), respectively, in the validation dataset.

Conclusion: The predictive model constructed based on lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter exhibited good performance in predicting LNM in gastric cancer and should aid the lymph node staging of gastric cancer and clinical decision-making.