Publications by authors named "M Nakamae"
We report the first large-scale retrospective cohort study on adolescent and young adult (AYA) polycythemia vera (PV) and essential thrombocythemia (ET) in Japan, a subgroup analysis using Japanese multicenter registry data (JSH-MPN-R18). This study included patients with PV (n = 31) or ET (n = 141) aged 20 to 39 years at the initial visit. Hemorrhage-free survival (HFS) was better in AYA ET than in non-AYA ET (5-year HFS: 100% vs.
View Article and Find Full Text PDFA case of posttransplant isolated extramedullary relapse of acute lymphoblastic leukemia achieving durable treatment-free remission with blinatumomab and donor lymphocyte infusion.
Makoto Nishijima Kentaro Ido Yusuke Okayama Hiroshi Okamura Masatomo Kuno Mika Nakamae Hirohisa Nakamae
Int J Hematol
November 2024
Article Synopsis
- Acute lymphoblastic leukemia (ALL) has poor outcomes when it relapses after stem cell transplants, but blinatumomab, a new treatment, shows promise, especially for advanced B-cell ALL.
- The case study highlights a 56-year-old woman who achieved a third complete remission (CR) with two cycles of blinatumomab after developing isolated extramedullary relapse (EMR) post-transplant.
- The patient remained free of complications and experienced a confirmed 2-year treatment-free remission, suggesting that blinatumomab is an effective option for treating EMR of B-ALL after allo-HCT due to its association with lower tumor burdens.
Article Synopsis
- Traditional relapse prediction models for post-transplant patients don't adapt based on new information collected after the transplant, making them less effective for treatment adjustments.
- This study focused on creating a dynamic relapse prediction model specifically for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) patients after allogeneic hematopoietic cell transplantation (allo-HCT), utilizing WT1mRNA levels from blood tests.
- The new model demonstrated significantly better predictive performance compared to older models, allowing for real-time predictions of relapse risk and is accessible through a user-friendly web application.