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Oncology Reports
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Print ISSN: 1021-335X Online ISSN: 1791-2431
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June-2026 Volume 55 Issue 6

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International Journal of Molecular Medicine

International Journal of Molecular Medicine

International Journal of Molecular Medicine is an international journal devoted to molecular mechanisms of human disease.

International Journal of Oncology

International Journal of Oncology

International Journal of Oncology is an international journal devoted to oncology research and cancer treatment.

Molecular Medicine Reports

Molecular Medicine Reports

Covers molecular medicine topics such as pharmacology, pathology, genetics, neuroscience, infectious diseases, molecular cardiology, and molecular surgery.

Oncology Reports

Oncology Reports

Oncology Reports is an international journal devoted to fundamental and applied research in Oncology.

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine is an international journal devoted to laboratory and clinical medicine.

Oncology Letters

Oncology Letters

Oncology Letters is an international journal devoted to Experimental and Clinical Oncology.

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Explores a wide range of biological and medical fields, including pharmacology, genetics, microbiology, neuroscience, and molecular cardiology.

Molecular and Clinical Oncology

Molecular and Clinical Oncology

International journal addressing all aspects of oncology research, from tumorigenesis and oncogenes to chemotherapy and metastasis.

World Academy of Sciences Journal

World Academy of Sciences Journal

Multidisciplinary open-access journal spanning biochemistry, genetics, neuroscience, environmental health, and synthetic biology.

International Journal of Functional Nutrition

International Journal of Functional Nutrition

Open-access journal combining biochemistry, pharmacology, immunology, and genetics to advance health through functional nutrition.

International Journal of Epigenetics

International Journal of Epigenetics

Publishes open-access research on using epigenetics to advance understanding and treatment of human disease.

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Review Open Access

Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)

  • Authors:
    • Hui Cheng
    • Guodong Zheng
    • Yuanyuan Yang
    • Chun Xu
    • Gusheng Tang
    • Chongmei Huang

  • View Affiliations / Copyright

    Affiliations: Department of Hematology, Changhai Hospital, Naval Medical University, Shanghai 200433, P.R. China, Department of VIP, Changhai Hospital, Naval Medical University, Shanghai 200433, P.R. China, Cellsee (Wuxi) Intelligent Technology Co., Ltd, Jiangsu 214000, P.R. China, Department of Hematology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
    Copyright: © Cheng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 115
    |
    Published online on: April 15, 2026
       https://doi.org/10.3892/or.2026.9120
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Abstract

Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic observation of cell morphology based on the French‑American‑British classification remains a fundamental diagnostic method for ALs. However, manual screening from bone marrow smear images is often inefficient, laborious and prone to subjective bias, leading to potential misdiagnosis or missed diagnosis. Artificial intelligence (AI), particularly machine learning (ML), has expanded human capabilities in analyzing complex datasets, leading to breakthroughs in multiple fields, including medical research and clinical practice. Increasingly, ML applications are being developed to diagnose hematological diseases by extracting and aggregating morphological characteristics from peripheral blood and bone marrow smears. However, applying ML methods to recognize cell morphology in hematological diseases presents unique challenges compared with other pathology subspecialties. The present review provided an overview of AI and ML applications in ALs diagnosis, focusing on cell segmentation and data mining methods from microscopy images, and highlights their advantages over manual microscopy.

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Copy and paste a formatted citation
Spandidos Publications style
Cheng H, Zheng G, Yang Y, Xu C, Tang G and Huang C: Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review). Oncol Rep 55: 115, 2026.
APA
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., & Huang, C. (2026). Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review). Oncology Reports, 55, 115. https://doi.org/10.3892/or.2026.9120
MLA
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., Huang, C."Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)". Oncology Reports 55.6 (2026): 115.
Chicago
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., Huang, C."Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)". Oncology Reports 55, no. 6 (2026): 115. https://doi.org/10.3892/or.2026.9120
Copy and paste a formatted citation
x
Spandidos Publications style
Cheng H, Zheng G, Yang Y, Xu C, Tang G and Huang C: Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review). Oncol Rep 55: 115, 2026.
APA
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., & Huang, C. (2026). Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review). Oncology Reports, 55, 115. https://doi.org/10.3892/or.2026.9120
MLA
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., Huang, C."Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)". Oncology Reports 55.6 (2026): 115.
Chicago
Cheng, H., Zheng, G., Yang, Y., Xu, C., Tang, G., Huang, C."Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review)". Oncology Reports 55, no. 6 (2026): 115. https://doi.org/10.3892/or.2026.9120
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