The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes

Koji Sasaki, Elias J. Jabbour, Farhad Ravandi, Marina Konopleva, Gautam Borthakur, William G. Wierda, Naval Daver, Koichi Takahashi, Kiran Naqvi, Courtney DiNardo, Guillermo Montalban-Bravo, Rashmi Kanagal-Shamanna, Ghayas Issa, Preetesh Jain, Jeffrey Skinner, Mary B. Rios, Sherry Pierce, Kelly A. Soltysiak, Junya Sato, Guillermo Garcia-ManeroJorge E. Cortes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Extreme gradient boosting methods outperform conventional machine-learning models. Here, we have developed the LEukemia Artificial intelligence Program (LEAP) with the extreme gradient boosting decision tree method for the optimal treatment recommendation of tyrosine kinase inhibitors (TKIs) in patients with chronic myeloid leukemia in chronic phase (CML-CP). A cohort of CML-CP patients was randomly divided into training/validation (N = 504) and test cohorts (N = 126). The training/validation cohort was used for 3-fold cross validation to develop the LEAP CML-CP model using 101 variables at diagnosis. The test cohort was then applied to the LEAP CML-CP model and an optimum TKI treatment was suggested for each patient. The area under the curve in the test cohort was 0.81899.Backward multivariate analysis identified age at diagnosis, the degree of comorbidities, and TKI recommended therapy by the LEAP CML-CP model as independent prognostic factors for overall survival. The bootstrapping method internally validated the association of the LEAP CML-CP recommendation with overall survival as an independent prognostic for overall survival. Selecting treatment according to the LEAP CML-CP personalized recommendations, in this model, is associated with better survival probability compared to treatment with a LEAP CML-CP non-recommended therapy. This approach may pave a way of new era of personalized treatment recommendations for patients with cancer.

Original languageEnglish (US)
Pages (from-to)241-250
Number of pages10
JournalAmerican Journal of Hematology
Volume96
Issue number2
DOIs
StatePublished - Feb 1 2021

ASJC Scopus subject areas

  • Hematology

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