ArticleNPJ precision oncology2021
Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features.
Article in NPJ precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- EfficientNet-B0-Based Screening of WBC-Diff Scattergram Images for Hematological Abnormalities.Cancer medicine · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.Leukemia · 2026Article
- Common Hematologic Emergencies-Acute Promyelocytic Leukemia and Microangiopathic Hemolytic Anemias-A Pivotal Role of Clinical Laboratory.International journal of laboratory hematology · 2026Review
- Multimodal AI reading of genetic complexity and residual marrow composition in acute promyelocytic leukemia.Frontiers in immunology · 2026Article
- Review
- A critical analysis of CellaVision systems in the modern hematology laboratory.American journal of clinical pathology · 2025Review
- Applications of Artificial Intelligence in Acute Promyelocytic Leukemia: An Avenue of Opportunities? A Systematic Review.Journal of clinical medicine · 2025Review
- Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia.NPJ precision oncology · 2025Article
- Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.Cancer science · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Microfluidic impedance flow cytometer leveraging virtual constriction microchannel and its application in leukocyte differential.Microsystems & nanoengineering · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Explainable AI identifies diagnostic cells of genetic AML subtypes.PLOS digital health · 2023Article
- Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.Scientific reports · 2023Article
- Cell projection plots: A novel visualization of bone marrow aspirate cytology.Journal of pathology informatics · 2023Article
- Case Report: Successful therapy with all-Frontiers in oncology · 2022Article
- Arsenic and all-Haematologica · 2021Article
- Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features.NPJ precision oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. By applying both cell-level and patient-level classification linked to t(15;17) PML/RARA ground-truth, we demonstrate that deep learning is capable of distinguishing APL in both discovery and prospective independent cohort of patients. Furthermore, we extract learned information from the trained network to identify previously undescribed morphological features of APL. The deep learning method we describe herein potentially allows a rapid, explainable, and accurate physician-aid for diagnosing APL at the time of presentation in any resource-poor or -rich medical setting given the universally available peripheral smear.
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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.