Functional MRI parametric map-based radiomics nomograms for preoperative risk stratification of early-stage endometrial cancer: A multicenter study.

OBJECTIVES: To evaluate the performance of nomograms combining clinical factors, apparent diffusion coefficient (ADC), and radiomics features from functional MRI parametric maps in predicting deep myometrial invasion (DMI), high histopathological grade, and lymphovascular space invasion (LVSI) in early endometrial cancer (EC). METHODS: This multicenter study recruited 362 patients with EC undergoing preoperative MRI. Radiomics features were extracted from four functional MRI parametric maps and selected via a three-step process. High-risk factors were identified by logistic regression. Nonradiomics models (clinical high-risk factors plus ADC), radiomics models (radiomics features), and nomogram models combining both were developed and validated for predicting DMI, high histopathological grade, and LVSI. Performance was assessed by receiver operating characteristic analysis. RESULTS: Logistic regression identified age and ADC as predictors of DMI, tumor size and ADC of high-grade lesions, and CA125 and ADC of LVSI; 10, 13, and 12 radiomics features constituted the respective Radscores. In the external testing set, the areas under the curve (AUCs) of the nonradiomics, radiomics, and nomogram models were 0.691 (95% confidence interval [CI]: 0.578-0.804), 0.756 (95% CI: 0.665-0.848), and 0.785 (95% CI: 0.709-0.849) for predicting DMI; 0.718 (95% CI: 0.613-0.823), 0.876 (95% CI: 0.796-0.956), and 0.906 (95% CI: 0.846-0.948) for high histopathological grade; and 0.651 (95% CI: 0.545-0.756), 0.764 (95% CI: 0.653-0.875), and 0.808 (95% CI: 0.716-0.900) for LVSI. CONCLUSION: The nomogram combining radiomics features from functional MRI parametric maps with clinical factors and ADC performs promisingly in evaluating DMI, high histopathological grade, and LVSI in early EC, demonstrating potential for personalized management.