Neoadjuvant Chemoradiotherapy in Locally Advanced Gastric Adenocarcinoma: Long-Term Results and Statistical Algorithm to Predict Individual Risk of Relapse

Abstract

Background: The purpose of this study was to evaluate the long-term outcomes of patients with locally advanced gastric adenocarcinoma (LAGC) intended to receive induction chemotherapy, chemoradiation and surgery and to develop an algorithm to estimate the individual risk of relapse in a population-based setting.

Methods: Patients with LAGC (cT3-4 and/or N+) were retrospectively evaluated. A pathological response was graded according to the Becker criteria. The nodal regression grade was assessed by a 4-point scale (A-D). A comprehensive analysis of 155 individual patient variables was performed, and logistic regression (LR) was utilized to develop a predictive model for relapse risk.

Results: From 2010 to 2024, 48 patients were analyzed. After a median follow-up of 49 months (range, 12-212), the 5-year actuarial PFS and OS rates were 44% and 48%, respectively. Four variables were identified as the most relevant features for training the LR model. Scores for the model accuracy, sensitivity and specificity (mean +/- sd) were 0.79 +/- 0.12, 0.74 +/- 0.221 and 0.88 +/- 0.14, respectively. For a validation dataset, the figures were 0.78, 0.88 and 0.73, respectively.

Conclusions: This neoadjuvant strategy seems to correlate with a favorable long-term outcome in a subset of intestinal-type LAGA patients who achieve ypN0 features.

Keywords: induction chemotherapy; locally advanced gastric cancer; logistic regression; long-term outcome; preoperative chemoradiation.

Figure 1

Receiver operating characteristic (ROC) curves of the logistic regression (LR) model’s predictive ability for the 6-fold cross-validation. The LR model was trained to predict the target variable, which is a dichotomous variable representing the risk of relapse or no risk of relapse. To achieve this goal, the model employs the predictor variables listed in Table 2.