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| PET-kinetiikka× | Radiomiikka× | |
|---|---|---|
| Tieteenala | Lääketieteellinen kuvantaminen | Lääketieteellinen kuvantaminen |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1983 | 2012 |
| Kehittäjä≠ | Christoph Patlak | Philippe Lambin |
| Tyyppi≠ | Mathematical framework for tracer kinetics in PET imaging | Machine learning-based texture and morphology analysis |
| Alkuperäislähde≠ | Patlak, C. S., Blasberg, R. G., Fenstermacher, J. D. (1983). Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Journal of Cerebral Blood Flow & Metabolism, 3(1), 1-7. DOI ↗ | 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. DOI ↗ |
| Rinnakkaisnimet | PET pharmacokinetics, Dynamic PET, PET compartmental modeling | texture analysis, radiomics analysis, quantitative imaging biomarkers |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | PET kinetic modeling is a quantitative analysis technique that tracks the temporal behavior of radioactive tracers in tissue to extract physiological parameters such as blood flow, metabolic rate, and receptor density. Established by Patlak, Logan, and Gunn in the 1980s and 1990s, kinetic modeling transforms raw PET time-activity curves into interpretable biological measures. It is widely used in neurology, oncology, and cardiology to assess disease severity, treatment response, and regional tissue function. | 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. |
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