A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC p...

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Bibliographic Details
Main Authors: Camelia Alexandra Coada, Miriam Santoro, Vladislav Zybin, Marco Di Stanislao, Giulia Paolani, Cecilia Modolon, Stella Di Costanzo, Lucia Genovesi, Marco Tesei, Antonio De Leo, Gloria Ravegnini, Dario De Biase, Alessio Giuseppe Morganti, Luigi Lovato, Pierandrea De Iaco, Lidia Strigari, Anna Myriam Perrone
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Cancers
Subjects:
endometrial cancer
pre-surgical risk
recurrence
prognosis
radiomic
artificial intelligence
Online Access:https://www.mdpi.com/2072-6694/15/18/4534

Internet

https://www.mdpi.com/2072-6694/15/18/4534

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