Preoperative MRI and immunohistochemical examination for the prediction of high-risk endometrial cancer.

Background Magnetic resonance imaging (MRI) and immunohistochemical (IHC) examination provides useful information for the risk stratification of endometrial cancer (EC). However, the use of the combination of MRI and IHC for the prediction of high-risk EC is controversial. The aim of this study was to evaluate the value of preoperative MRI and IHC examination in prediction of patients with high-risk EC. Methods This retrospective case-control study was conducted from January 1, 2018 to May 1, 2021 at two hospitals. A primary cohort (n=102) comprised patients with histologically confirmed EC in one hospital between January 1, 2018 and May 31, 2020. An additional external cohort (n=35) comprising patients with histologically confirmed EC in a different hospital from January 1, 2020 to May 1, 2021 was included for validation. Imaging features including tumor size, tumor margin, relative T2 value, tumor signal intensity on diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) were determined from preoperative MRI images. IHC markers including ER, PR, p53 and Ki67 were determined through IHC staining of preoperative curettage specimen. Patients were divided into high-risk and low-intermediate- risk group based on the final histological results. Differences between categorical and numerical variables were assessed using chi-square test and independent-sample t-test, respectively. Multivariate binary logistic regression analyses were used for construction of the prediction model A fusion prediction model was constructed by combining MRI features and IHC markers. The predictive performance of the model was then validated using the external cohort. Results Imaging and IHC markers were significantly associated with risk ranks. Model 1 based on MRI features showed an area under the curve (AUC) of 0.822 [95% confidence interval (CI), 0.741-0.903] whereas Model 2 based on IHC markers showed an AUC of 0.894 (95% CI, 0.829-0.960). Notably, model 3 integrating independent MRI and IHC risk factors demonstrated good calibration and high differentiation ability with an AUC of 0.958 (95% CI, 0.923-0.993), and showed good discrimination with an AUC of 0.84 (95% CI, 0.677-0.942) using the external validation set. Conclusions This study proposes a comprehensive predictive model comprising MRI and IHC features as a powerful tool for preoperative risk stratification to assist in clinical decision-making for EC patients.