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The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.

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Annals of hematology 📖 저널 OA 100% 2025: 19/19 OA 2026: 152/152 OA 2025~2026 2026 Vol.105(4) p. 124 OA
Retraction 확인
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PICO 자동 추출 (휴리스틱, conf 3/4)

유사 논문
P · Population 대상 환자/모집단
3273 patients aged 40–69 with AML in CR.
I · Intervention 중재 / 시술
RIC and 769 patients with low scores and who received MAC were categorized into the optimal conditioning group (896, 71
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
In conclusion, we developed an easy-to-use model that helps the physician choose a patient-specific conditioning regimen for patients with AML in CR.

Shimomura Y, Komukai S, Kitamura T, Sobue T, Yamasaki S, Kondo T

📝 환자 설명용 한 줄

[UNLABELLED] This study aimed to identify patient groups in which myeloablative conditioning (MAC) or reduced-intensity conditioning (RIC) regimens induced superior progression-free survival (PFS) in

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↓ .bib ↓ .ris
APA Shimomura Y, Komukai S, et al. (2026). The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.. Annals of hematology, 105(4), 124. https://doi.org/10.1007/s00277-026-06881-w
MLA Shimomura Y, et al.. "The optimal conditioning intensity of stem cell transplantation for acute myeloid leukemia in complete remission.." Annals of hematology, vol. 105, no. 4, 2026, pp. 124.
PMID 41711974 ↗

Abstract

[UNLABELLED] This study aimed to identify patient groups in which myeloablative conditioning (MAC) or reduced-intensity conditioning (RIC) regimens induced superior progression-free survival (PFS) in patients with acute myeloid leukemia (AML) in complete remission (CR) using a machine-learning approach. Our study included 3273 patients aged 40–69 with AML in CR. The patients were divided into training ( = 2020) and validation cohorts ( = 1253). We employed a machine learning-based group identification model in the training cohort. Subsequently, in the validation cohort, we estimated the impact of the optimal conditioning group compared with the non-optimal conditioning group on PFS using an inverse probability weight analysis. The developed model was consistent with the eight factors and combinations, and the high score suggested that RIC was more appropriate than MAC. In the validation cohort, 127 patients with high scores and who received RIC and 769 patients with low scores and who received MAC were categorized into the optimal conditioning group (896, 71.5%). The weighted hazard ratio for PFS was 0.73 (95% confidence interval: 0.57–0.94) in the optimal conditioning group compared with the non-optimal conditioning group ( = 0.016). In conclusion, we developed an easy-to-use model that helps the physician choose a patient-specific conditioning regimen for patients with AML in CR.

[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1007/s00277-026-06881-w.

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