Predicting c-KIT Inhibitor Efficacy in Patient-Derived Models of Sinonasal Mucosal Melanomas through Integrated Histogram Analysis of Whole-Tumor DKI, IVIM, and DCE-MRI.

Abstract

Purpose: To evaluate whole-tumor histogram analysis of diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI), in predicting the efficacy of imatinib, a c-KIT inhibitor, for treating patient derived models derived from sinonasal mucosal melanomas (MMs).

Experimental design: This study included 38 patients with histologically confirmed sinonasal MM, who underwent DKI, IVIM, and DCE-MRI. Patient-derived tumor xenograft (PDX) models and precision-cut tumor slices (PCTS) were established to evaluate tumor response to imatinib. Whole-tumor histogram analysis was conducted on imaging parameters, and logistic regression models were applied to determine the predictive value of these metrics in differentiating responders from non-responders.

Results: Among the 38 sinonasal MM patients, 12 were classified as responders and 26 as non-responders based on PDX and PCTS model responses to imatinib. The DKI model revealed significant differences in mean, median, P10, and P90 values of Dk and K between responders and non-responders (P < 0.05). The IVIM model indicated significant differences in P10 and mean values of D, with kurtosis f being a strong predictor. The DCE-MRI model, using the P90 Ktrans metric, demonstrated robust predictive performance, achieving an AUC of 0.89, with 80.77% specificity and 91.67% sensitivity. The combined logistic model integrating DKI, IVIM, and DCE-MRI metrics produced the highest predictive accuracy, with an AUC of 0.90.

Conclusions: Whole-tumor histogram analysis of DKI, IVIM, and DCE-MRI offers a non-invasive method for predicting the efficacy of c-KIT inhibitors in sinonasal MMs, presenting valuable implications for guiding targeted treatment in this rare cancer type.