مقایسهٔ روش‌ها

	رادیومیکس	آنژیوگرافی OCT
حوزه	تصویربرداری پزشکی	تصویربرداری پزشکی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2012	2012
پدیدآور	Philippe Lambin	Yali Jia
نوع	Machine learning-based texture and morphology analysis	Optical imaging technique for vasculature visualization
منبع بنیادین	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.	Jia, Y., Tan, O., Tokayer, J., et al. (2012). Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Optics Express, 20(4), 4710-4725.
نام‌های دیگر	texture analysis, radiomics analysis, quantitative imaging biomarkers	OCTA, OCT-A
مرتبط	5	5
خلاصه	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.	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.