Hassan Awada, MD, of the Taussig Cancer Institute, Cleveland Clinic Foundation, discusses the use of newer machine-learning techniques to help decipher a set of prognostic subgroups that could predict survival, thus potentially improving on traditional methods and moving acute myeloid leukemia into the era of personalized medicine (Abstract 34).
Meletios A. Dimopoulos, MD, of the University of Athens, discusses data from the phase III APOLLO study, which evaluated the use of subcutaneous daratumumab plus pomalidomide...
Farhad Ravandi, MD, of The University of Texas MD Anderson Cancer Center, offers his expert perspective on key treatment studies in acute myeloid leukemia on the use of gilte...
Sara Zarnegar-Lumley, MD, of Vanderbilt University Medical Center, discusses an analysis of a large cohort confirming the age-associated prevalence of IDH mutations ...
Nitin Jain, MD, of The University of Texas MD Anderson Cancer Center, reviews six important abstracts on CAR T-cell treatments for B-cell acute lymphoblastic leukemia (ALL): ...
Corey Cutler, MD, MPH, of Dana-Farber Cancer Institute, discusses results from a multicenter trial that compared reduced-intensity allogeneic hematopoietic cell transplantati...