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| 광간섭단층촬영 혈관조영술× | 방사선체학(Radiomics)× | |
|---|---|---|
| 분야 | 의료영상 | 의료영상 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 2012 | 2012 |
| 창시자≠ | Yali Jia | Philippe Lambin |
| 유형≠ | Optical imaging technique for vasculature visualization | Machine learning-based texture and morphology analysis |
| 원전≠ | Jia, Y., Tan, O., Tokayer, J., et al. (2012). Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Optics Express, 20(4), 4710-4725. 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 ↗ |
| 별칭≠ | OCTA, OCT-A | texture analysis, radiomics analysis, quantitative imaging biomarkers |
| 관련 | 5 | 5 |
| 요약≠ | Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that visualizes the microvasculature in the retina and choroid by detecting motion contrast from flowing blood. Developed by Jia and colleagues in 2012, OCTA uses repeated OCT scans of the same tissue location to identify blood flow based on the decorrelation signal. It has become a critical diagnostic tool in ophthalmology for detecting retinal and macular diseases without requiring fluorescein injection. | 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. |
| ScholarGate데이터셋 ↗ |
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