SMART LAB

Research

AI-powered Diagnosis

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Finding integrated imaging biomarker for cancer
AI-based Radio-Patho-Genomics for Precision Medicine
  • Biomarkers as major prognostic factors such as immuno-chemotherapy were generally identified from genome or pathological results, but recent studies to find prognostic biomarkers within medical images (MR/CT, PET, etc.) are ongoing.
  • The advantages of medical image analysis are as follows:
    1. Cross-sectional analysis is possible, not sampling-based analysis
    2. Intra/peri-tumoral analysis is possible extensively
    3. It contains objective information based on specific physical quantities. (Example: electron density in CT)
  • Analysis using medical images enables extracting information on the entire tumor area in a cross-sectional way which provides an opportunity to quantitatively evaluate tumor heterogeneity, one of the main characteristics of malignant tumors.
  • The radiomic feature that analyzes the texture of the tumor defines intra-tumor heterogeneity by quantifying the image pattern based on the spatial relationship between pixels of a specific intensity or co-occurrence of pixel values.
  • This makes it possible to derive and quantify an imaging phenotype close to a specific disease phenotype.
  • Thus, It is expected that more detailed patient risk stratification will be possible through multi-omics analysis incorporating biomarkers of each level.