Comparison of Conventional Gadoxetate Disodium–Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion
Yidi Chen, Yuwei Xia, Parag Tolat, Liling Long, Zijian Jiang, Huang Zhongkui, Qin Tang
Abstract
ADC value, nonsmooth tumor margin, and 20-minute T1 relaxation time show high diagnostic accuracy for predicting MVI. Radiomics signatures with machine learning can further improve the ability to predict MVI and are best modeled during HBP. The SVM, XGBoost, and LR classifiers may serve as potential biomarkers to evaluate MVI.
Topics & Concepts
MedicineRadiomicsNuclear medicineRadiologyRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisAdvanced X-ray and CT Imaging