AUTHOR=Zhu Yongjian , Jiang Zhichao , Wang Bingzhi , Li Ying , Jiang Jun , Zhong Yuxin , Wang Sicong , Jiang Liming 

TITLE=Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion−Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.841460

DOI=10.3389/fonc.2022.841460

ISSN=2234-943X

ABSTRACT=Background: This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients’ prognosis, so as to provide a basis for clinical individualized precision treatment.

Methods: One hundred and twenty-nine newly diagnosed LAGC patients underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this. Pathological tumor regression grade (TRG) served as reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological response (pR) and pathological non-response (pNR) groups were analyzed. Uni and multivariate logistic regression were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS were compared using the Kaplan-Meier method and the log-rank test.

Results: Sixty-nine patients were classified as pR and 60 were pNR. Ktrans, kep and ve values in pR group were significantly higher, while ADCstandard and D values were significantly lower than those in pNR group. Multivariate logistic regression analysis demonstrated that postoperative pathological stage (ypStage), Ktrans, kep, ve, and D values were independent predictor for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an AUC of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by IVIM model were independent predictors of poor RFS. IVIM-DWI model could significantly stratify median RFS (52 months vs.15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC.

Conclusion: Pretreatment DCE-MRI quantitative parameters Ktrans, kep, ve and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI and their combination could help RFS stratification of LAGC patients.