ZOOMit diffusion kurtosis imaging combined with diffusion weighted imaging for the assessment of microsatellite instability in endometrial cancer

Purpose: Detecting microsatellite instability (MSI) plays a key role in the management of endometrial cancer (EC), as it is a critical predictive biomarker for Lynch syndrome or immunotherapy response. A pressing need exists for cost-efficient, broadly accessible tools to aid patient for universal testing. Herein, we investigate the value of ZOOMit diffusion kurtosis imaging (DKI) and diffusion weighted imaging (DWI) based on preoperative pelvic magnetic resonance imaging (MRI) images in assessing MSI in EC.

Methods: Preoperative MRI examination including ZOOMit DKI and DWI of 81 EC patients were retrospectively analyzed. The apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD) and the largest tumor size based on MRI images, as well as patients' clinicopathological features were compared and analyzed according to different microsatellite statuses.

Results: Of the 81 patients, 59 (72.8%) who were microsatellite stability (MSS) and 22 (27.2%) who were MSI. Interobserver agreement for the quantitative parameter measurements was excellent (ICC 0.78-0.98). The ADC and MD values were significantly lower, while Ki-67 proliferation level and MK values were significantly higher in the MSI group compared to those of the MSS group. The parameters of MD and MK were independent predictors for determining MSI, and their combination showed better diagnostic efficacy with an area under the receiver operating characteristic curve (AUROC) of 0.860 (95% confidence interval, 0.765, 0.927), although there was no significant difference compared to each individual parameter.

Conclusion: The microstructural heterogeneity assessment of ZOOMit DKI allowed for characterizing MSI status in EC. Within the current universal MSI testing paradigm, DKI may provide added value as a potential noninvasive imaging biomarker for preoperative assessment of MSI tumors, thereby facilitating clinical decision-making.