ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2021
Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
90 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effect of RAS Pathway Gene Mutations on Survival in Myelodysplastic Syndrome: A Systematic Review and Meta-Analysis.Cancer control : journal of the Moffitt Cancer CenterPooled it
- Magrolimab Plus Azacitidine Versus Placebo Plus Azacitidine in Patients With Untreated Higher-Risk Myelodysplastic Syndromes: The Phase III ENHANCE Study.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Trial
- Prognostic impact of peripheral blood WT1-mRNA expression in patients with MDS.Blood cancer journal · 2019Trial
- Review
- A 2026 update on myelodysplastic neoplasms: current state, challenges and future directions.Nature reviews. Clinical oncology · 2026Review
- A novel approach to defining progression in MDS and precursor myeloid conditions in the MDS Natural History Study.Blood advances · 2026Observational
- Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- The evolving nosology of myeloid neoplasms: the semi-centennial of the 1976 French-American-British classification.Leukemia · 2025Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Myelodysplastic syndromes: Updates on Genomic Landscape, Molecular Subtypes, & Targeted Therapies.Current hematologic malignancy reports · 2025Review
- An unconquered challenge in MDS: review of pathophysiology, clinical manifestations, and management options of MDS with thrombocytopenia.Annals of hematology · 2025Review
- Promises and challenges of artificial intelligence in haematological diagnostics.British journal of haematology · 2025Review
- Review
- Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.British journal of haematology · 2025Article
- A clinico-genomic prognostic model for primary myelodysplastic neoplasm in Asia.Blood cancer journal · 2025Article
- Cooperative progression of colitis and leukemia modulated by clonal hematopoiesis via PTX3/IL-1β pro-inflammatory signaling.Genes & diseases · 2025Article
- Advances and challenges in the treatment of myelodysplastic syndromes.Experimental hematology & oncology · 2025Review
- Progress in the Genetics of Myelodysplastic Syndromes with a Latin American Perspective.Genes · 2025Review
- How I treat higher-risk MDS.Blood · 2025Review
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
purposePatients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies.
methodsA total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index.
resultsThe median age for the training cohort was 71 years. Commonly mutated genes included
conclusionA personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.
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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.