PREDICTING THE PROBABILITY OF REGIONAL LYMPH NODE METASTASES IN GASTRIC CANCER ACCORDING TO CLINICAL DATA
Abstract
Background. Limited diagnostic accuracy of traditional preoperative imaging techniques for gastric cancer N-staging leads to inappropriate treatment planning. Hence the necessity to develop and apply prognostic models that allow for the prediction of metastatic lesions in regional lymph nodes. Objective. To develop a clinical data-based model for preoperative prediction of metastatic lesions in regional lymph nodes (pN+) in gastric cancer (GC). Material and methods. A retrospective analysis of radical treatment outcomes in 1054 patients with GC was performed. To develop a prognostic model based on linear logistic regression, the total patient sample was randomly divided into test and training cohorts. The model on the test sample included prognostic factors that demonstrated their discriminatory ability based on several selection algorithms. Clinical validation of the model was carried out according to the assessment of long-term treatment outcomes. Results. Risk factors for pN+ include: age – odds ratio (OR) is 1.02 (95% CI 1.0–1.04 per year), p=0.040; primary tumor size (natural logarithm) – OR is 1.8 (95% CI 1.4–2.4), p<0.001; infiltrative variant of macroscopic growth form – OR is 1.9 (95% CI 1.3–2.9), p=0.001; non-cohesive variant of adenocarcinoma – OR is 1.6 (95% CI 1.0–2.4), p=0.051; suspected metastatic lesions of regional lymph nodes according to preoperative assessment – OR is 4.0 (95% CI 2.6–6.2), p<0.001. There has been developed a prognostic model, concordance index (AUC for cohort tests) being 0.778 (95% CI 0.739–0.820). Conclusion. The application of the developed prognostic model with due regard to the clinical and morphological features of the neoplastic process, as well as patient's age allows for more accurate preoperative N-staging. This in turn contributes to optimizing management strategies for non-metastatic GC patients due to appropriate preoperative anti-cancer treatment planning.
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