Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis

Purpose: To evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometrial adenocarcinoma. Materials and methods: Seventy-three women (mean age: 66 ± 11.5 [SD] years; range: 45–88 years) with endometrial adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD® software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC). Results: A total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC] = 0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC = 0.59). Volumes and tumor short axis were greater for high-grade tumors (P = 0.0002 and P = 0.004, respectively) and for patients with LVSI (P = 0.004 and P = 0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis ≥ 20 mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC = 0.86). Conclusion: MRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis ≥ 20 mm is the best predictor of high grade and LVSI.