Radiomics of Multiparametric MRI in Tumor Grading of Endometrial Cancer

Random forest models were constructed to predict tumor grade (grade 1-2 vs. grade 3) of endometrial cancer based on radiomics features extracted from quantitative T1, T2, proton density maps generated by synthetic MRI and apparent diffusion coefficient maps generated by diffusion-weighted imaging. The classification model based on features extracted from all the quantitative maps achieved the highest area under the curve of 0.804 compared to models constructed based on single quantitative map.