Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study - Bi, Qiu; Wang, Yaoxin; Deng, Yuchen; Liu, Yang; Pan, Yuanrui; Song, Yang; Wu, Yunzhu; Wu, Kunhua (Frontiers in Oncology, 2022)
Purpose: To evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. Methods: A total of 371 patients were divided into the training group (245 patients from center A), internal validation group (82 patients from center A), and external validation group (44 patients from centers B). The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, 9 machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models. Results: LR model was the optimal radiomics model (AUC=0.910 and 0.798, respectively) with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups, and outperformed the clinical model (AUC=0.739 and 0.592, respectively) or radiologist (AUC=0.768 and 0.628, respectively). The nomogram (AUC=0.917 and 0.802, respectively) achieved better discrimination performance than that of the optimal radiomics model in two validation groups. The stacking model (AUC=0.915) and ensemble model (AUC=0.918) had similar performance compared with the nomogram in the internal validation group. Whereas, the AUCs of the stacking model (AUC=0.792) and ensemble model (AUC=0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits. Conclusions: Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram present excellent and stable diagnostic efficiency.