方法对比

	成像质谱流式细胞术	影像组学
领域	医学影像	医学影像
方法族	Process / pipeline	Process / pipeline
起源年份	2014	2012
提出者	Bernd Bodenmiller	Philippe Lambin
类型	Multiplexed single-cell imaging by mass spectrometry	Machine learning-based texture and morphology analysis
开创性文献	Giesen, C., Wang, H. A., Schapiro, D., et al. (2014). Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods, 11(4), 417-422.	Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. Nature Reviews Clinical Oncology, 9(12), 676-684.
别名	IMC, mass cytometry, multiplex ion beam imaging, MIBI	texture analysis, radiomics analysis, quantitative imaging biomarkers
相关	5	5
摘要	Imaging Mass Cytometry (IMC) is a multiplexed proteomics technique that maps the subcellular localization of up to 40-50 proteins in tissue sections simultaneously using mass spectrometry detection. Developed by Bodenmiller and colleagues in 2014, IMC combines the single-cell imaging power of immunofluorescence with the multiplexing capacity of mass cytometry, enabling comprehensive analysis of cell types, states, and spatial interactions within tissue microenvironments. IMC has emerged as a powerful tool in immuno-oncology, immunobiology, and tissue biology for dissecting cellular heterogeneity and spatial organization.	Radiomics is a computational methodology that extracts large numbers of quantitative features from medical images (CT, MRI, PET) using automated image analysis and machine learning to discover imaging biomarkers associated with disease phenotype, prognosis, and treatment response. Developed by Lambin, Gillies, and colleagues in 2012, radiomics aims to decode the biology underlying visible imaging patterns, enabling personalized medicine through image-based phenotyping. It has emerged as a powerful tool in oncology for tumor characterization, prognosis prediction, and therapy response assessment.