Trial reportAmerican journal of hematology2021
The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes.
Trial report in American journal of hematology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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.
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.
Who cites it
19 citing papers in PubMed, 34 citations in OpenAlex.
- Chronic Myeloid Leukemia: Historical Perspective, Pathophysiology, and Treatment Advances.Acta haematologica · 2026Review
- Review
- A Framework for Locally Imputing and Predicting Biomarker Trajectories Under Irregular Monitoring: Application to Chronic Myeloid Leukemia.Research square · 2026Article
- Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia.International journal of molecular sciences · 2025Review
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- The biology of chronic myeloid leukemia: an overview of the new insights and biomarkers.Frontiers in oncology · 2025Review
- Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.BMC cancer · 2024Article
- Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?Cancers · 2024Review
- Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma.Cancer research communications · 2023Article
- Applications of Machine Learning in Chronic Myeloid Leukemia.Diagnostics (Basel, Switzerland) · 2023Review
- Liquid biopsies and minimal residual disease in lymphoid malignancies.Frontiers in oncology · 2023Review
- Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements.The EPMA journal · 2022Review
- Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.BMC medical informatics and decision making · 2022Article
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- Chromosomal Instability in Chronic Myeloid Leukemia: Mechanistic Insights and Effects.Cancers · 2022Review
- Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering.Cancers · 2022Article
- Adverse events and dose modifications of tyrosine kinase inhibitors in chronic myelogenous leukemia.Frontiers in oncology · 2022Review
- Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance.Cancers · 2021Review
- Current status and novel strategy of CML.International journal of hematology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 3 institutions in 3 countries.
Funding
Abstract
Extreme gradient boosting methods outperform conventional machine-learning models. Here, we have developed the LEukemia Artificial intelligence Program (LEAP) with the extreme gradient boosting decision tree method for the optimal treatment recommendation of tyrosine kinase inhibitors (TKIs) in patients with chronic myeloid leukemia in chronic phase (CML-CP). A cohort of CML-CP patients was randomly divided into training/validation (N = 504) and test cohorts (N = 126). The training/validation cohort was used for 3-fold cross validation to develop the LEAP CML-CP model using 101 variables at diagnosis. The test cohort was then applied to the LEAP CML-CP model and an optimum TKI treatment was suggested for each patient. The area under the curve in the test cohort was 0.81899.Backward multivariate analysis identified age at diagnosis, the degree of comorbidities, and TKI recommended therapy by the LEAP CML-CP model as independent prognostic factors for overall survival. The bootstrapping method internally validated the association of the LEAP CML-CP recommendation with overall survival as an independent prognostic for overall survival. Selecting treatment according to the LEAP CML-CP personalized recommendations, in this model, is associated with better survival probability compared to treatment with a LEAP CML-CP non-recommended therapy. This approach may pave a way of new era of personalized treatment recommendations for patients with cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.