ArticleHemaSphere2025
Integrating transcriptomic profiling and machine learning: A clinically actionable prognostic model for infant acute myeloid leukemia.
Article in HemaSphere, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.BMC geriatrics · 2026Article
- Recent advances in deep learning for leukemia diagnosis: a scoping review of diagnostic modalities and fusion-based approaches.Frontiers in digital health · 2026Review
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Authors and funding
6 authors.
Funding
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Abstract
Infant acute myeloid leukemia (AML), particularly in those under 3 years of age, presents poor prognostic outcomes and distinct biological characteristics that require age-specific risk assessment. This study, utilizing data from four pediatric AML (pAML) trials conducted by the Children's Oncology Group, aimed to develop a simple RNA expression-based prognostic model to refine risk stratification for infant AML. Expression data from 213 infant AML patients were analyzed using machine-learning algorithms to develop the infant-prognostic-score (IPSscore), or IPSgroup when categorized. To validate the stability of the model, internal validation was conducted on a set of 127 cases, and external validation was performed using a separate set of 63 patients from a different ethnic background. Furthermore, we compared its prognostic prediction capability with that of other AML models and explored its potential clinical decision-making value for infant AML patients. The IPSgroup independently and specifically predicted outcomes in infant AML, outperforming several previously published RNA expression-based models. Infant patients categorized into the high-risk group based on IPSgroup may benefit from hematopoietic stem cell transplantation (HSCT), while those in the low-risk group are not suitable for HSCT. Additionally, when combined with the current pAML stratification system used in clinical trials, the IPSgroup enabled re-stratification of 43% of infant AML patients into more accurate risk groups, highlighting the advantage of incorporating gene expression analysis into clinical decision-making. Infant AML demonstrates significant heterogeneity at clinical, molecular, and prognostic levels. The newly proposed model surpasses existing AML stratifications, offering a valuable tool for clinical decision-making and treatment strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.