Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

Purpose: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. Methods: A tra...

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Bibliographic Details
Main Authors: Xingfeng Li, Michele Dessi, Diana Marcus, James Russell, Eric O. Aboagye, Laura Burney Ellis, Alexander Sheeka, Won-Ho Edward Park, Nishat Bharwani, Sadaf Ghaem-Maghami, Andrea G. Rockall
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Cancers
Subjects:
endometrial cancer
T2-weighted MRI
radiomics
machine learning classification
feature selection
deep myometrial infiltration
Online Access:https://www.mdpi.com/2072-6694/15/8/2209

Internet

https://www.mdpi.com/2072-6694/15/8/2209

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