Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to creat...

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Bibliographic Details
Main Authors: Jesus Gonzalez-Bosquet, Sofia Gabrilovich, Megan E. McDonald, Brian J. Smith, Kimberly K. Leslie, David D. Bender, Michael J. Goodheart, Eric Devor
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:International Journal of Molecular Sciences
Subjects:
endometrial cancer
recurrence
prediction
machine learning
Online Access:https://www.mdpi.com/1422-0067/23/24/16014

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