Biological Imaging in Tumor Microenvironment
A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".
Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 799
Special Issue Editor
Interests: computational pathology; artificial intelligence; neoplastic progression; tumor heterogeneity; spatial biological imaging
Special Issue Information
Dear Colleagues,
The tumor microenvironment comprises a heterogeneous population of tumor cells, immune cells, stromal cells, blood vessels, and extracellular matrix. The comprehensive profiling of compositions and interactions among distinct entities within the tumor microenvironment allows for a thorough understanding of tumor heterogeneity, progression, metastasis, therapeutic responses, resistances, etc. Biological imaging is among the most effective techniques for studying tumor microenvironments. Besides the structure and function, the context information of tissues and cells obtained from biological imaging enables the interrogation of the spatial relationships among bio-entities. Novel underexplored tissue and cellular patterns via biomedical image analysis have the potential to provide new insights. Artificial intelligence (AI) methods, such as deep learning, have revolutionized tissue/cell detection, segmentation, and recognition in biological imaging. Currently, AI-powered biological imaging analysis is an emerging topic in cancer research. This Special Issue aims to cover new advances in the analysis of biological imaging, including, but not limited to, microscopy imaging, fluorescence imaging, multiplex imaging, and nuclear imaging, for a better understanding of tumor microenvironments and their applications to tumor detection, diagnosis, prognosis, and treatment.
Dr. Pingjun Chen
Guest Editor
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Keywords
- tumor microenvironment
- tumor heterogeneity
- biological imaging
- biomedical image analysis
- whole slide imaging
- multiplex imaging
- nuclear imaging
- computational pathology
- spatial biology
- artificial intelligence
- deep learning
- predictive biomarkers
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