ArticleScientific reports2017
Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.Pediatric research · 2025Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- Pediatric leukemia: origins, pathogenesis, the role of microenvironment and immunological modulation.Frontiers in immunology · 2026Review
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Efficient convolutional neural networks for acute lymphoblastic leukaemia prediction in computer vision.Scientific reports · 2025Article
- AutoML identification of microRNA biomarkers in high-risk pediatric acute lymphoblastic leukemia.Non-coding RNA research · 2025Article
- Deep Learning for Predicting Late-Onset Breast Cancer Metastasis: The Single-Hyperparameter Grid Search (SHGS) Strategy for Meta-Tuning a Deep Feed-Forward Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- A Decade of Transformation in the Management of Childhood Acute Lymphoblastic Leukemia: From Conventional Chemotherapy to Precision Medicine.Pediatric reports · 2025Review
- Identification of Molecular Subtypes of B-Cell Acute Lymphoblastic Leukemia in Mexican Children by Whole-Transcriptome Analysis.International journal of molecular sciences · 2025Article
- Article
- Revolutionizing precision oncology: the role of artificial intelligence in personalized pediatric cancer care.Frontiers in medicine · 2025Review
- Prediction of total iodine dose of I-131 therapy for Graves' hyperthyroidism achieved remission status: a random forest regressor model approach to assess treatment efficacy.Frontiers in endocrinology · 2025Article
- Review
- Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.BMC medical informatics and decision making · 2024Article
- Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.Blood advances · 2024Article
- Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery.Annals of surgical oncology · 2023Article
- A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters.Journal of cellular and molecular medicine · 2023Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cross-validation was used to rank clinical variables on the randomly split training sets of 336 newly diagnosed ALL children, and a forward feature selection algorithm was employed to find the shortest list of most discriminatory variables. To enable an unbiased estimation of the prediction model to new patients, besides the split test sets of 150 patients, we introduced another independent data set of 84 patients to evaluate the model. The Random Forest model with 14 features achieved a cross-validation accuracy of 0.827 ± 0.031 on one set and an accuracy of 0.798 on the other, with the area under the curve of 0.902 ± 0.027 and 0.904, respectively. The model performed well across different risk-level groups, with the best accuracy of 0.829 in the standard-risk group. To our knowledge, this is the first study to use machine learning models to predict childhood ALL relapse based on medical data from Electronic Medical Record, which will further facilitate stratification treatments.
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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.