Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer

Abstract Background: Different molecular or genetic information influences the clinical decisions for patients diagnosed with endometrial cancer (EC). A non-invasive, precise, and efficient preoperative evaluation method is crucial for the prognosis of patients with EC. Purpose: The aim of this study was to construct MRI-based radiomics models to predict immunohistochemical biomarkers and assess the relationship between radiomic features and the Ki-67 proliferation rate in EC. Material and Methods: We retrospectively analyzed 100 estrogen receptor (ER), 94 progesterone receptor (PR), 97 P53, and 98 Ki-67 immunohistochemistry cases with EC who underwent magnetic resonance imaging (MRI) between May 2012 and June 2023 prior to surgery. Radiomic features were individually extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and the apparent diffusion coefficient (ADC). Least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. And logistic regression was employed to construct radiomics models with 5-fold cross-validation. The receiver operating characteristic (ROC) curves were analyzed to evaluate the performance of the radiomics models. Finally, Pearson's correlations were utilized to explore the association between the values of selected features and the Ki-67 proliferation rate. Results: A total of 2264 features were extracted from each patient’s MRI sequences. The selected features from the multi-sequence models were shared with or without the single sequence models. Both single sequence and multi-sequence models demonstrated good diagnostic performance, although the diagnostic performance of multi-sequence models outperformed the single sequence models. Correlation analysis showed that adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis and t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness were negatively correlated with the Ki-67 proliferation rate. Conclusions: MRI-based radiomic features are promising predictors of immunohistochemistry and prognosis in EC.