Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model

• DOI: 10.1007/s10278-023-00936-4
• Publication Date: 2024-01-18T21:29:48Z
• Authors: Huan Meng; Yu-Feng Sun; Yu Zhang; Ya-Nan Yu; Jing Wang; Jia-Ning Wang; Lin-Yan Xue; Xiao-Ping Yin
• Keywords: Risk Stratification

Endometrial carcinoma (EC) risk stratification prior to surgery is crucial for clinical treatment. In this study, we intend evaluate the predictive value of radiomics models based on magnetic resonance imaging (MRI) and staging early-stage EC. The study included 155 patients who underwent MRI examinations were pathologically diagnosed with EC between January, 2020, September, 2022. Three-dimensional features extracted from segmented tumor images captured by scans (including T2WI, CE-T1WI delayed phase, ADC), 1521 each three modalities. Then, using five-fold cross-validation a multilayer perceptron algorithm, these filtered Pearson's correlation coefficient develop prediction model performance was assessed analyzing ROC curves calculating AUC, accuracy, sensitivity, specificity. terms stratification, CE-T1 sequence demonstrated highest accuracy 0.858 ± 0.025 an AUC 0.878 0.042 among sequences. However, combining all sequences resulted in enhanced reaching 0.881 0.040, corresponding increase 0.862 0.069. context staging, utilization combination involving T2WI led notably elevated 0.956 0.020, surpassing achieved when employing any singular feature. Correspondingly, 0.979 0.022. When incorporating concurrently, reached 0.000, accompanied 0.986 0.007. It noteworthy that level surpassed radiologist, which stood at 0.832. has potential accurately predict early