A machine learning model of response to hypomethylating agents in myelodysplastic syndromes

Nathan Radakovich, David A. Sallman, Rena Buckstein, Andrew Brunner, Amy Dezern, Sudipto Mukerjee, Rami Komrokji, Najla Al-Ali, Jacob Shreve, Yazan Rouphail, Anne Parmentier, Alexandre Mamedov, Mohammed Siddiqui, Yihong Guan, Teodora Kuzmanovic, Metis Hasipek, Babal Jha, Jaroslaw P. Maciejewski, Mikkael A. Sekeres, Aziz Nazha

Research output: Contribution to journalArticlepeer-review

Abstract

Hypomethylating agents (HMA) prolong survival and improve cytopenias in individuals with higher-risk myelodysplastic syndrome (MDS). Only 30-40% of patients, however, respond to HMAs, and responses may not occur for more than 6 months after HMA initiation. We developed a model to more rapidly assess HMA response by analyzing early changes in patients’ blood counts. Three institutions’ data were used to develop a model that assessed patients’ response to therapy 90 days after the initiation using serial blood counts. The model was developed with a training cohort of 424 patients from 2 institutions and validated on an independent cohort of 90 patients. The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 in the train/test group and 0.84 in the validation group. The model provides cohort-wide and individual-level explanations for model predictions, and model certainty can be interrogated to gauge the reliability of a given prediction.

Original languageEnglish (US)
Article number104931
JournaliScience
Volume25
Issue number10
DOIs
StatePublished - Oct 21 2022

Keywords

  • Drugs
  • artificial intelligence
  • cancer

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'A machine learning model of response to hypomethylating agents in myelodysplastic syndromes'. Together they form a unique fingerprint.

Cite this