Advancements in Hematopathology Diagnostic Techniques
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Laboratory Medicine".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1852
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, the field of hematopathology has experienced significant progress in novel diagnostic techniques. These include immunohistochemistry, flow cytometry, advanced molecular techniques, and next-generation sequencing. These techniques have revolutionized the diagnosis, risk stratification, and monitoring of hematologic malignancies (minimal residual disease) leading to more accurate and personalized treatment options, including target drugs. The recently updated WHO classification of hematological malignancies is based on integrating these techniques (integrated diagnostic).
Moreover, the incorporation of these diagnostic techniques with artificial intelligence and machine learning algorithms exhibits significant potential for enhancing the efficiency and precision of hematopathology diagnosis. These technologies can assist pathologists in automating data analysis and interpretation, resulting in accelerated and more precise diagnosis. Continued advancements in technology, diagnostic techniques and understanding of hematologic diseases are expected to further enhance the accuracy and efficiency of diagnosis. Moreover, the integration of multidimensional datasets, such as genomics, proteomics, phenotypic and clinical data, will enable a more comprehensive characterization of hematologic diseases. The generation of these algorithms could also in predict the response to different treatment options in the future.
The primary objective of this Special Issue is to offer valuable insights into the contemporary advancements and practical utilization of diagnostic techniques in hematopathology. Additionally, we aim to discuss the future prospects of these techniques, emphasizing the need for ongoing research and development to improve patient outcomes in hematologic malignancies.
Dr. José Antonio García-Vela
Guest Editor
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Keywords
- hematopathology
- flow cytometry
- next generation sequencing (NGS)
- minimal residual disease (MRD)
- machine learning algorithms
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