A Personalized Risk Model for Azacitidine Outcome in Myelodysplastic Syndrome and Other Myeloid Neoplasms Identified by Machine Learning Model Utilizing Real-World Data

A Personalized Risk Model for Azacitidine Outcome in Myelodysplastic Syndrome and Other Myeloid Neoplasms Identified by Machine Learning Model Utilizing Real-World Data

Azacitidine is an approved therapy for higher-risk myelodysplastic syndrome (MDS). However, only 30–40% patients respond to azacitidine, and the responses may take up to six cycles to become evident. Delayed responses and the myelosuppressive effects of azacitidine make it challenging to predict whi...

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Bibliographic Details
Main Authors: Kirsty Sharplin, William Proudman, Rakchha Chhetri, Elizabeth Ngoc Hoa Tran, Jamie Choong, Monika Kutyna, Philip Selby, Aidan Sapio, Oisin Friel, Shreyas Khanna, Deepak Singhal, Michelle Damin, David Ross, David Yeung, Daniel Thomas, Chung H. Kok, Devendra Hiwase
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Cancers
Subjects:
azacitidine
prognostication
MDS
survival
machine learning
Online Access:https://www.mdpi.com/2072-6694/15/16/4019

Internet

https://www.mdpi.com/2072-6694/15/16/4019

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