User login
Key clinical point: Personalized treatment selection according to the LEukemia Artificial intelligence Program (LEAP) recommendations for patients with chronic myeloid leukemia in chronic phase (CML-CP) is associated with better likelihood of survival.
Major finding: The LEAP CML-CP recommendation was associated with an improved overall survival (P less than 001).
Study details: A cohort of CML-CP patients was randomly assigned to training/validation (n = 504) and test cohorts (n = 126). The training/validation cohort was used 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 tyrosine kinase inhibitor therapy was selected for each patient.
Disclosures: The study was supported by the University of Texas MD Anderson Cancer Center Support Grant from the National Institutes of Health, the National Institutes of Health/National Cancer Institute under award, the University of Texas MD Anderson MDS/AML Moon Shot, and Leukemia Texas. K Sasaki, EJ Jabbour, F Ravandi, M Konopleva, G Garcia-Manero, JE Cortes, C DiNardo reported relationships with various pharmaceutical companies. The remaining authors declared no conflicts of interest.
Source: Sasaki K et al. Am J Hematol. 2020 Nov 12. doi: 10.1002/ajh.26047.
Key clinical point: Personalized treatment selection according to the LEukemia Artificial intelligence Program (LEAP) recommendations for patients with chronic myeloid leukemia in chronic phase (CML-CP) is associated with better likelihood of survival.
Major finding: The LEAP CML-CP recommendation was associated with an improved overall survival (P less than 001).
Study details: A cohort of CML-CP patients was randomly assigned to training/validation (n = 504) and test cohorts (n = 126). The training/validation cohort was used 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 tyrosine kinase inhibitor therapy was selected for each patient.
Disclosures: The study was supported by the University of Texas MD Anderson Cancer Center Support Grant from the National Institutes of Health, the National Institutes of Health/National Cancer Institute under award, the University of Texas MD Anderson MDS/AML Moon Shot, and Leukemia Texas. K Sasaki, EJ Jabbour, F Ravandi, M Konopleva, G Garcia-Manero, JE Cortes, C DiNardo reported relationships with various pharmaceutical companies. The remaining authors declared no conflicts of interest.
Source: Sasaki K et al. Am J Hematol. 2020 Nov 12. doi: 10.1002/ajh.26047.
Key clinical point: Personalized treatment selection according to the LEukemia Artificial intelligence Program (LEAP) recommendations for patients with chronic myeloid leukemia in chronic phase (CML-CP) is associated with better likelihood of survival.
Major finding: The LEAP CML-CP recommendation was associated with an improved overall survival (P less than 001).
Study details: A cohort of CML-CP patients was randomly assigned to training/validation (n = 504) and test cohorts (n = 126). The training/validation cohort was used 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 tyrosine kinase inhibitor therapy was selected for each patient.
Disclosures: The study was supported by the University of Texas MD Anderson Cancer Center Support Grant from the National Institutes of Health, the National Institutes of Health/National Cancer Institute under award, the University of Texas MD Anderson MDS/AML Moon Shot, and Leukemia Texas. K Sasaki, EJ Jabbour, F Ravandi, M Konopleva, G Garcia-Manero, JE Cortes, C DiNardo reported relationships with various pharmaceutical companies. The remaining authors declared no conflicts of interest.
Source: Sasaki K et al. Am J Hematol. 2020 Nov 12. doi: 10.1002/ajh.26047.