Evidence map›Paper›PMID 42324513›Full record

ArticleBMC geriatrics2026

Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.

Ling Zhang, Jiang Liu, Jingjing Liang, Xialin Zhang, Jialong Xin, Mingxuan Wei, Lina Wang, Jiaxin Huo, Chunxia Dong, Yuan Li and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ling Zhang *Department of Hematology, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Jiang Liu *Department of Hematology, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Jingjing Liang *The First Clinical Medical College, Shanxi Medical University, Taiyuan, 030001, China.
Xialin ZhangDepartment of Hematology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
Jialong XinCollege of Computer Science and Technology (College of Data Science ), Taiyuan University of Technology, Taiyuan, 030600, China.
Mingxuan WeiXi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Lina WangDepartment of Hematology, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital, Taiyuan, 030032, China.
Jiaxin HuoSchool of Computer Science and Technology (Big Data School), North University of China, Taiyuan, 030051, China.
Chunxia DongDepartment of Hematology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, China.
Yuan LiDepartment of Hematology, Houma City People's Hospital, Houma, 043000, China.
Yan QiangSchool of Software, North University of China, Taiyuan, 030051, China.
Junyan ZhangDepartment of Clinical Epidemiology and Evidence-Based Medicine, Shanxi Bethune Hospital, Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, Taiyuan, 030032, China. richard.zhang@both-win.net.
Ruijuan ZhangDepartment of Hematology, First Hospital of Shanxi Medical University, Taiyuan, 030001, China. 13593169668@163.com.

Funding

Health Commission of Shanxi Province Grant No. 2025ZD018
6 · The paper itself

Abstract

backgroundElderly patients with acute myeloid leukemia (AML) exhibit considerable biological and clinical heterogeneity, hindering precise prognosis. Existing prognostic systems inadequately capture the complexity of elderly AML due to their reliance on data from younger cohorts and omission of key factors like immunophenotypic markers and therapeutic profiles. This study aimed to develop and internally validate a machine learning-based prognostic model specifically tailored to elderly AML patients.

methodsA total of 156 patients were analyzed using a two-stage modeling strategy. Clinical and genomic variables were modeled first, followed by independent analysis of immunophenotypic features. Feature selection was performed using multilayer perceptron (MLP) and random forest (RF), while multivariate Cox regression was used for final model construction. Internal validation was conducted using 1000 bootstrap iterations to assess model stability and performance.

resultsThe model demonstrated strong predictive performance, with a concordance index (C-index) of 0.702. Time-dependent area under the curve (AUC) and calibration plots confirmed accurate prediction of 1-, 3-, and 5-year overall survival. Decision curve analysis indicated favorable net benefit across a range of threshold probabilities. Key independent prognostic factors identified included TP53 mutations, high CD13 expression, and IDH2 mutations.

conclusionThis model provides a robust and interpretable tool for individualized risk stratification in elderly AML. By integrating genomic, immunophenotypic, and therapeutic variables, it may help optimize treatment decisions and improve outcomes for this vulnerable population. Future efforts should focus on external validation and integration of dynamic biomarkers.

Indexed as

GenomicsImmunophenotypingLeukemia, Myeloid, AcuteMachine LearningAgedAged, 80 and overFemaleHumansMaleMultilayer PerceptronsPredictive Learning ModelsPrognosisRandom ForestRisk AssessmentAcute myeloid leukemiaBootstrap resamplingElderlyMachine learningPrognostic modelRisk stratification

Identifiers

PMID42324513
PMCPMC13536640

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.