Abstract

Cancer is a complex disease characterized by heterogeneous and dynamic cellular behavior. Understanding the cellular states of cancer cells is crucial for effective diagnosis and treatment. Recent advancements in imaging technologies and computational analysis have revealed that cellular morphology holds valuable information about cancer cell states. This article explores the predictive markers contained within cellular morphological signatures and their potential implications for cancer research and clinical applications. Cellular morphology, encompassing size, shape, texture and spatial organization, provides insights into underlying cellular processes. Abnormalities in cell size and shape, as well as nuclear and cytoplasmic morphology, are commonly observed in cancer cells. Quantitative analysis of these features can distinguish cancer cells from normal cells and provide crucial information about tumor aggressiveness and metastatic potential. Moreover, the spatial arrangement of cancer cells within the tumor microenvironment can indicate tumor invasion, angiogenesis and immune cell infiltration. To uncover predictive markers within cellular morphological signatures, sophisticated computational analysis and machine learning techniques are employed. By training predictive models on annotated datasets, researchers can identify the most informative morphological features for specific cancer cell states. These models can then be applied to classify and predict the state of cancer cells in new samples.

Keywords: Prognosis; Cell morphology; Cancer cell state; Tumor; Metastatic