Assessment of microvascular heterogeneity between tumor and proximal tumor-distant tissue in esophageal squamous cell carcinoma using pharmacokinetic parameters derived from dynamic contrast-enhanced MRI

LIAO Wenhan; OU Jing; SU Yanxia; Liao Xinyi; LI Jingke; ZHOU Haiying; LI Rui; CHEN Tianwu

doi:10.19300/j.2026.L22727

Abstract：Objective To investigate the feasibility of using pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)to differentiate microvascular heterogeneity between tumor tissue and proximal tumor-distant tissue(PTD)in esophageal squamous cell carcinoma(ESCC).Methods A total of 154 patients with pathologically confirmed ESCC from two medical centers were prospectively enrolled.There were 128 patients from Center A were randomly assigned to a training set(102 cases)and an internal validation set(26 cases)at a ratio of 8∶2.Patients from Center B(26 cases)served as an external validation set.The training set was used for selection of pharmacokinetic parameters reflecting microvascular heterogeneity and for Logistic regression model construction,while the validation sets were used to assess the diagnostic performance of the model.Regions of interest(ROIs)were delineated in tumor tissue and PTD using medical imaging processing software developed by United Imaging Healthcare.Sensitivity analyses were performed to assess the robustness of the model by comparing ROI delineation results with and without exclusion of necrotic and cystic areas.The mean,standard deviation(SD),and coefficient of variation(CV)of the reflux rate constant(kep),volume transfer constant(Ktrans),and extracellular extravascular volume fraction(ve)were extracted.The Wilcoxon signed-rank test was used to compare differences in pharmacokinetic parameters between tumor tissue and PTD.Parameters with statistically significant differences were included in multivariate Logistic regression analysis.Receiver operating characteristic(ROC)curves were used to evaluate the discriminative performance of single parameters and multivariable models,and the area under the curve(AUC)was calculated.DeLong test was applied to compare differences in AUCs.Net reclassification improvement(NRI)and integrated discrimination improvement(IDI)were calculated to assess reclassification ability of the model.Results In the training set,the mean of kep,SD of kep,mean of Ktrans,mean of ve,and CV of ve showed significant differences between tumor tissue and PTD(all P<0.05).Multivariable Logistic regression analysis identified the mean of kep and the CV of ve as independent predictors for differentiating tumor tissue from PTD(both P<0.05),and a multivariable model was constructed based on these two parameters.The AUC values of the multivariable model in the training,internal validation,and external validation sets were 0.835,0.846,and 0.818,respectively.The DeLong test showed that the AUCs of the multivariable model were significantly higher than those of the mean of kep(all P<0.05),while there was no significant difference between the AUCs of the multivariable model and those of the CV of ve(all P>0.05).The multivariable model performed better than the mean of kep and comparably to the CV of ve,whereas NRI and IDI indicated that the multivariable model had superior reclassification ability(all P<0.05).Sensitivity analysis showed that whether necrotic or cystic regions were excluded from the ROIs or not,the discriminative performance of the model and the direction of parameter effects remained consistent.Conclusion The multivariable model based on the CV of ve and the mean of kep demonstrates good performance in distinguishing ESCC tumor tissue from PTD.