পদ্ধতির তুলনা করুন

	রেডিওমিক্স	অক্ট অ্যাঞ্জিওগ্রাফি
ক্ষেত্র	চিকিৎসা প্রতিবিম্বন	চিকিৎসা প্রতিবিম্বন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2012	2012
প্রবর্তক	Philippe Lambin	Yali Jia
ধরন	Machine learning-based texture and morphology analysis	Optical imaging technique for vasculature visualization
মৌলিক উৎস	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.
অপর নাম	texture analysis, radiomics analysis, quantitative imaging biomarkers	OCTA, OCT-A
সম্পর্কিত	5	5
সারসংক্ষেপ	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.