About this Research Topic
Hematologic malignancies are at the forefront of the molecular revolution. Novel tools for diagnosis, prognosis and targeted therapies are changing the landscape of how physicians and scientists tackle these tumors. Given the diversity of these tools, which can range from image-based analysis to epigenetics and protein-based studies, the most clinically interesting ones may not be easily found in one publication.
This call for papers will emphasize the need for new ways of analyzing clinically relevant issues in malignant hematology. Tools may not themselves be newly introduced but the way in which they have been employed has given new perspective on how to diagnose and treat these tumors. Our goal is to find connecting themes in these approaches and the way that such tools move our field forward. Articles can be original research, focused reviews of the literature, or case series with short reviews.
- Molecular findings that improve targeted therapeutics for hematologic malignancies
- Improved prognostication of lymphoma outcomes using machine learning algorithms
- Discovery of new biomarkers for accurate subtyping in leukemias
- Altered microenvironment in lymphomas and plasma cell neoplasms that allow improved efficacy with immune based therapies
- Genetic markers that demonstrate clonal relationships and evolution between hematologic neoplasms with different phenotypes
- Tracking and analyzing the effects of cell based therapies in hematologic malignancies
This call for papers will emphasize the need for new ways of analyzing clinically relevant issues in malignant hematology. Tools may not themselves be newly introduced but the way in which they have been employed has given new perspective on how to diagnose and treat these tumors. Our goal is to find connecting themes in these approaches and the way that such tools move our field forward. Articles can be original research, focused reviews of the literature, or case series with short reviews.
- Molecular findings that improve targeted therapeutics for hematologic malignancies
- Improved prognostication of lymphoma outcomes using machine learning algorithms
- Discovery of new biomarkers for accurate subtyping in leukemias
- Altered microenvironment in lymphomas and plasma cell neoplasms that allow improved efficacy with immune based therapies
- Genetic markers that demonstrate clonal relationships and evolution between hematologic neoplasms with different phenotypes
- Tracking and analyzing the effects of cell based therapies in hematologic malignancies
Keywords: hematologic malignancies, novel biomarkers, diagnostic tools, next generation, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
