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Biomedical informatics and panomics for evidence‐based radiation therapy

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More than half of all cancer patients receive ionizing radiation as part of their treatment. Treatment outcomes are determined by complex interactions between cancer genetics, treatment regimens, and patient‐related variables. A key component of modern radiation oncology research is to predict at the time of treatment planning or during the course of fractionated radiation treatment, the probability of tumor eradication and normal tissue risks for the type of treatment being considered for the individual patient. A typical radiotherapy treatment scenario can generate a large pool of panomics data that may comprise 3D/4D anatomical and functional imaging information (noted as radiomics), in addition to biological markers (genomics, proteomics, metabolomics, etc.) derived from peripheral blood and tissue specimens. Radiotherapy data informatics constitutes a unique interface between physical and biological processes. It can benefit from the general advances in biomedical informatics research while still requires the development of its own technologies within this framework to address specific issues related to its unique physics–biology interface. We review recent advances and discuss current challenges to interrogate panomics data in radiotherapy using bioinformatics tools for data aggregation, sharing, visualization, and outcomes modeling. We provide examples based on our and others experiences using systems radiobiology and machine learning to develop predictive models of outcomes in radiotherapy. We also highlight the potential opportunities in this field for evidence‐based personalized medicine research for bioinformaticians and clinical decision‐makers. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Health Care
Understanding of heterogeneous variables interactions as a feedback loop into the treatment planning system to adaptively improve patient's outcomes.
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Graph‐based approach for identifying robust biomarkers from limited proteomics sample‐size data. The example shows the identification of alpha‐2‐macroglobulin (α2M) by analyzing proteomics data of 3 × 3 matched pair patients with and without radiation pneumonitis (RP) using a novel graph‐based approach.
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Top: A Bayesian network with probability tables for combined biomarker proteins and physical variables for modeling local tumor control in lung cancer. Bottom: The binning boundaries for each variable.
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Multiscale‐modeling framework of tissue (tumor) radiation response along the time and space axes.
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