Preoperative prediction of microsatellite instability in gastric cancer using quantitative multiparametric MRI with...

To develop and validate a non-invasive preoperative predictive model utilizing quantitative parameters from dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) for determining microsatellite instability (MSI) status in gastric cancer.

This prospective study enrolled 244 patients with pathologically confirmed MSI status (30 MSI-H, 214 MSI-L/MSS), determined via immunohistochemistry. Two radiologists independently analyzed preoperative MRI blinded to clinicopathological results. Quantitative parameters were derived from DCE-MRI pharmacokinetic modeling (K^trans, V_p,V_e, K_ep) and multiple diffusion models (ADC, D, D*, f, MD, MK, DDC, α). Inter-observer agreement was assessed using the intraclass correlation coefficient (ICC). Statistical comparisons were performed using t-tests or Mann-Whitney U tests. Multivariate logistic regression was conducted to identify independent predictors and build predictive models.

Significant differences in several parameters, including K^trans, V_e, ADC, MD, DDC and D*, were observed between MSI-H and MSI-L/MSS cohorts (P < 0.05). The DCE-MRI model, featuring K^trans as an independent predictor, yielded an area under the curve (AUC) of 0.715. The DWI model, with ADC as an independent predictor, achieved an AUC of 0.740. The comprehensive mpMRI model, which integrated both DCE-MRI and DWI parameters, demonstrated superior preoperative predictive performance, attaining an AUC of 0.841, which was significantly higher than that of any single-parameter model (P < 0.05).

Quantitative parameters derived from DCE-MRI and DWI offer a non-invasive and effective approach for preoperatively predicting MSI-H status in gastric cancer, with potential clinical applications in personalized treatment planning.