Four main steps in applying artificial intelligence to analysis of immunogenomics, radiomics, and pathomics data regarding the tumor immune microenvironment. (IMAGE)
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Step 1, data collection: immunogenomics (genomics/transcriptomics), radiomics, and digital pathology data from the real world are appropriately collected and stored. Step 2, data processing: data from various sources undergo several processing steps, including data cleaning to remove inconsistencies, data normalization to standardize values, data augmentation to enhance dataset diversity, and data splitting to create training and testing sets, thus ensuring quality and consistency for analysis and model development. Step 3, feature extraction and analysis: deep learning and machine learning algorithms are used to identify, quantify, and analyze relevant patterns, characteristics, and relationships within datasets for predictive modeling. Step 4, integration and application: extracted features are combined with clinical data to build predictive models and comprehensive systems that enhance diagnosis, treatment planning, and personalized patient care through advanced analysis.
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Xi Wei, Tianjin Medical University Cancer Institute & Hospital
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