ArticleComputational and structural biotechnology journal2022
Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Dual molecular and clinical machine-learning prognostic modeling in pancreatic ductal adenocarcinoma: a chaperone-mediated autophagy-based framework integrating multi-cohort molecular signatures and a single-center clinical nomogram.Frontiers in cell and developmental biology · 2026Article
- Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients.Annals of medicine · 2025Article
- Chorionic Gonadotropin Beta 7 is a marker of immune evasion in cancer.bioRxiv : the preprint server for biology · 2025Article
- Biomarkers and Mental Disorders: A Relevance Analysis Using a Random Forest Algorithm.Biomolecules · 2025Article
- Multi-omics integration and machine learning-driven construction of an immunogenic cell death prognostic model for colon cancer and functional validation of FCGR2A.Frontiers in pharmacology · 2025Article
- Prediction of prognosis in elderly patients with chronic heart failure based on random survival forest.Frontiers in cardiovascular medicine · 2025Article
- Advancing precision medicine in immunoglobulin light-chain amyloidosis: a novel prognostic model incorporating multi-organ indicators.Internal and emergency medicine · 2024Article
- Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model.Microbiology spectrum · 2024Article
- Advances in next-generation sequencing for relapsed pediatric acute lymphoblastic leukemia: current insights and future directions.Frontiers in genetics · 2024Review
- Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.International journal of medical sciences · 2024Article
- DNA Copy Number Alterations and Copy Neutral Loss of Heterozygosity in Adult Ph-Negative Acute B-Lymphoblastic Leukemia: Focus on the Genes Involved.International journal of molecular sciences · 2023Article
- Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Using interpretable machine learning to extend heterogeneous antibody-virus datasets.Cell reports methods · 2023Article
- Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2023Article
- A prediction model for the 5-year, 10-year and 20-year mortality of medullary thyroid carcinoma patients based on lymph node ratio and other predictors.Frontiers in surgery · 2022Article
- Plasma metabolomic and lipidomic alterations associated with anti-tuberculosis drug-induced liver injury.Frontiers in pharmacology · 2022Article
- Deregulated calcium signaling in blood cancer: Underlying mechanisms and therapeutic potential.Frontiers in oncology · 2022Review
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3 authors.
Funding
Abstract
High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing interpretable genomic inputs to predict relapse/death in high-risk pediatric B-ALL patients. We utilized whole exome sequencing profiles from 156 patients in the TARGET-ALL study (with samples collected at presentation) further stratified into training and test cohorts (109 and 47 patients, respectively). To avoid overfitting and facilitate the interpretation of machine learning results, input genomic variables were engineered using a stepwise approach involving univariable Cox models to select variables directly associated with outcomes, genomic coordinate-based analysis to select mutational hotspots, and correlation analysis to eliminate feature co-linearity. Model training identified 7 genomic regions most predictive of relapse/death-free survival. The test cohort error rate was 12.47%, and a polygenic score based on the sum of the top 7 variables effectively stratified patients into two groups, with significant differences in time to relapse/death (log-rank P = 0.001, hazard ratio = 5.41). Our model outperformed other EFS modeling approaches including an RSF using gold-standard prognostic variables (error rate = 24.35%). Validation in 174 standard-risk patients and 3 patients who failed to respond to induction therapy confirmed that our RSF model and polygenic score were specific to high-risk disease. We propose that our feature selection/engineering approach can increase the clinical interpretability of RSF, and our polygenic score could be utilized for enhance clinical decision-making in high-risk B-ALL.
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