Reduced dose and shorter duration venetoclax regimens are effective for newly diagnosed acute myeloid leukemia patients not considered fit for intensive treatment.
We classified 45 AML patients not considered fit for intensive treatment into 3 groups: standard dose VEN (Group A), shorter duration of VEN (Group B), and dose reduction of VEN (Group C).
APA
Oka S, Ueda-Akagi Y, et al. (2026). Reduced dose and shorter duration venetoclax regimens are effective for newly diagnosed acute myeloid leukemia patients not considered fit for intensive treatment.. Leukemia & lymphoma, 67(3), 669-674. https://doi.org/10.1080/10428194.2025.2604566
MLA
Oka S, et al.. "Reduced dose and shorter duration venetoclax regimens are effective for newly diagnosed acute myeloid leukemia patients not considered fit for intensive treatment.." Leukemia & lymphoma, vol. 67, no. 3, 2026, pp. 669-674.
PMID
41447515
Abstract
We classified 45 AML patients not considered fit for intensive treatment into 3 groups: standard dose VEN (Group A), shorter duration of VEN (Group B), and dose reduction of VEN (Group C). CRc rates were 58, 73, and 72% in Groups A, B, and C, respectively. EFS and OS were significantly shorter in Group A than in Groups B and C. The incidence of FN and severe neutropenia was lower in Groups B (73% and 27%) and C (78% and 44%) than in Group A (92% and 67%). The number of treatment cycles was lower in Group A (7.5) than in Groups B (10.2) and C (15.5). The present study showed that shortening the duration and reducing the dose of VEN may reduce the risk of complications and be as effective as standard dose VEN for the treatment of AML patients not considered fit for intensive treatment.
MeSH Terms
Humans; Leukemia, Myeloid, Acute; Male; Female; Middle Aged; Bridged Bicyclo Compounds, Heterocyclic; Aged; Antineoplastic Combined Chemotherapy Protocols; Sulfonamides; Adult; Treatment Outcome; Retrospective Studies; Young Adult; Aged, 80 and over; Drug Administration Schedule; Neutropenia
같은 제1저자의 인용 많은 논문 (3)
- Revisiting AI Model Interpretability in Lung Cancer Screening: Challenges in Balancing Predictive Performance and Reliability.
- Revisiting AI Interpretability in Precision Oncology: Why Predictive Accuracy Does Not Ensure Stable Feature Importance.
- Complementing interpretable machine learning with synergistic analytical strategies for thyroid cancer recurrence prediction.