Preoperative prediction of parametrial invasion in cervical cancer using MRI

Abstract

Parametrial invasion (PMI) is a key factor to propose a treatment plan for cerrvical cancers. This study proposes a machine learning based PMI evaluation method to determine the presence of PMI in cervical cancer independent of the physician’s diagnostic experience. The proposed method requires the T2-weighted MR images, its cancer tumor mask, and dilated mask to compute radiomics features. The Lasso regression extracts important features, and they are used to train machine learning models and to classify PMI presence. Experiments on 10 PMI patients and 35 non-PMI patients showed that the logistic regression achieved the highest classification performance among the tested 8 classifiers. The comparison with physician’s assessments, the proposed method obtained superior results.