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

• Published in: American journal of hematology Vol. 96; no. 2; pp. 241 - 250
• Main Authors: Sasaki, Koji, Jabbour, Elias J., Ravandi, Farhad, Konopleva, Marina, Borthakur, Gautam, Wierda, William G., Daver, Naval, Takahashi, Koichi, Naqvi, Kiran, DiNardo, Courtney, Montalban‐Bravo, Guillermo, Kanagal‐Shamanna, Rashmi, Issa, Ghayas, Jain, Preetesh, Skinner, Jeffrey, Rios, Mary B., Pierce, Sherry, Soltysiak, Kelly A., Sato, Junya, Garcia‐Manero, Guillermo, Cortes, Jorge E.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2021; Wiley Subscription Services, Inc
• Subjects: Adolescent; Adult; Aged; Artificial Intelligence; Chronic myeloid leukemia; Diagnosis; Disease-Free Survival; Female; Hematology; Humans; Learning algorithms; Leukemia; Leukemia, Myelogenous, Chronic, BCR-ABL Positive - drug therapy; Leukemia, Myelogenous, Chronic, BCR-ABL Positive - mortality; Machine learning; Male; Medical prognosis; Middle Aged; Models, Biological; Multivariate analysis; Myeloid leukemia; Patients; Protein Kinase Inhibitors - administration & dosage; Protein-tyrosine kinase; Survival; Survival Rate
• ISSN: 0361-8609; 1096-8652
• DOI: 10.1002/ajh.26047

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.