ArticleBlood cancer discovery2021
Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS.
Article in Blood cancer discovery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML.Leukemia · 2022Trial
- Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges.Blood research · 2026Review
- Self-supervised learning can distinguish myelodysplastic neoplasms from clinical mimics using bone marrow biopsies.Blood neoplasia · 2026Article
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Review
- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Image-based explainable artificial intelligence accurately identifies myelodysplastic neoplasms beyond conventional signs of dysplasia.NPJ precision oncology · 2025Article
- Granulocyte abundance and maturation state at diagnosis predicts treatment-free remission in CML.Leukemia · 2025Article
- Promises and challenges of artificial intelligence in haematological diagnostics.British journal of haematology · 2025Review
- PredictingiScience · 2025Article
- The Role of Megakaryocyte Assessment in Bone Marrow Cytology.Journal of clinical medicine · 2025Review
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- European health regulations reduce registry-based research.Health research policy and systems · 2024Article
- Morphomics via next-generation electron microscopy.Journal of molecular cell biology · 2024Review
- Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.Cancer research · 2024Article
- Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.Pathobiology : journal of immunopathology, molecular and cellular biology · 2024Review
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Comparison of cytomorphology and histomorphology in myelodysplastic syndromes.Frontiers in oncology · 2024Article
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN), bone marrow (BM) histopathology is assessed to identify dysplastic cellular morphology, cellularity, and blast excess. Yet, other morphologic findings may elude the human eye. We used convolutional neural networks to extract morphologic features from 236 MDS, 87 MDS/MPN, and 11 control BM biopsies. These features predicted genetic and cytogenetic aberrations, prognosis, age, and gender in multivariate regression models. Highest prediction accuracy was found for SIGNIFICANCE: Histopathology is elementary in the diagnostics of patients with MDS, but its high-dimensional data are underused. By elucidating the association of morphologic features with clinical variables and molecular genetics, this study highlights the vast potential of convolutional neural networks in understanding MDS pathology and how genetics is reflected in BM morphology.
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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.