Objective: To investigate the predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters for the efficacy of neoadjuvant chemotherapy in locally advanced gastric cancer. Methods: Sixty-five patients with locally advanced gastric cancer (LAGC) confirmed by gastroscopy and received neoadjuvant chemotherapy (NCT) were enrolled in this study. Quantitative DCE-MRI was performed before NCT, and the quantitative parameters were measured, including volume transfer constant (K(trans)), rate constant (K(ep)), volume fraction of extravascular extracellular space (V(e)) and volume fraction of plasma (V(p)). After NCT, all patients received radical gastrectomy. According to postoperative pathological tumor regression grade, patients were divided into response group and non-response group, and the differences of DCE quantitative parameters between the two groups were compared. ROC curve was utilized to analyze the predictive efficacy of DCE quantitative parameters for NCT response of LAGC, and multivariate logistic regression analysis was performed to analyze the predictive efficacy of combined parameters. Results: Thirty-seven patients were in response group and 28 patients were in non-response group. The pretreatment K(trans) in the response group were [0.216 min(-1) (0.130 min(-1), 0.252 min(-1))], significantly higher than [0.091 min(-1) (0.069 min(-1), 0.146 min(-1))] of non-response group (P<0.001), and V(e) in the response group were [0.354(0.228, 0.463)], significantly higher than [0.200(0.177, 0.253)]of non-response group (P<0.001). ROC analysis showed the AUCS of K(trans) and V(e) in predicting NCT efficacy were 0.881 and 0.756, respectively. Multiple logistic regression analysis showed that the combination of the two parameters could improve the AUC to 0.921, with the sensitivity and specificity of 86.5% and 89.3%, respectively. Conclusion: DCE-MRI quantitative parameters could help to predict the NCT response of LAGC, and the combination of parameters could improve the predictive efficacy.
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