Prediction of stage, differentiation and Ki-67 status of locally advanced cervical cancer by DCE-MRI texture analysis

This retrospective study explored the value of texture analysis in predicting the stage, differentiation and Ki-67 status of pretreatment advanced cervical cancer. Multi-class radiomics feature extraction was performed on the maximum enhancement (ME) and maximum relative enhancement (MRE) maps from DCE-MRI. A prediction model using a machine learning-XGB classifier showed the mean sensitivities of predicting FIGOⅡb-Ⅲa, poor differentiation and high Ki-67 status were 0.767, 0.963 and 0.967; specificities were 0.958, 0.361 and 0.694 , and AUCs were 0.910, 0.920 and 0.840 respectively. DCE-MRI textural parameters have potential as non-invasive imaging biomarkers in predicting histopathology in advanced cervical cancer.