iScience (Oct 2022)

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

Journal volume & issue
Vol. 25, no. 10
p. 104931

Abstract

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Summary: 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.

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