TY - JOUR
T1 - The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase
T2 - A model to improve patient outcomes
AU - Sasaki, Koji
AU - Jabbour, Elias J.
AU - Ravandi, Farhad
AU - Konopleva, Marina
AU - Borthakur, Gautam
AU - Wierda, William G.
AU - Daver, Naval
AU - Takahashi, Koichi
AU - Naqvi, Kiran
AU - DiNardo, Courtney
AU - Montalban-Bravo, Guillermo
AU - Kanagal-Shamanna, Rashmi
AU - Issa, Ghayas
AU - Jain, Preetesh
AU - Skinner, Jeffrey
AU - Rios, Mary B.
AU - Pierce, Sherry
AU - Soltysiak, Kelly A.
AU - Sato, Junya
AU - Garcia-Manero, Guillermo
AU - Cortes, Jorge E.
N1 - Publisher Copyright:
© 2020 Wiley Periodicals LLC.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - 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.
AB - 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.
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U2 - 10.1002/ajh.26047
DO - 10.1002/ajh.26047
M3 - Article
C2 - 33180322
AN - SCOPUS:85096984339
SN - 0361-8609
VL - 96
SP - 241
EP - 250
JO - American Journal of Hematology
JF - American Journal of Hematology
IS - 2
ER -