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tumor diagnosis using radiomics. Radiomics is an explicit method for extracting a wide range of quantifiable characteristics from digital images that are not discernible to human eyes [ 8 ]. Radiomic features offer information on patterns of the range of
within a selected region of interest (ROI). It provides a method to measure intra-regional heterogeneity and to detect tissue changes, including subtle differences in textural information that would be imperceivable to the human eye. Radiomics analysis of
selected regions of interest (ROI) and analyze them with computational tools [ 1 ]. The basic concept of radiomic analysis is to generate a large number of quantitative imaging biomarkers from radiology images that can be linked to various phenotypes of the
and eventually improving overall clinical outcome. By quantifying coronary plaques and determining adverse plaque features, we gain access to a wider range of information which may improve plaque characterization [ 4 ]. Use of radiomics and machine
Térdporc szegmentálása MR-felvételekből mesterséges intelligencia segítségével
Segmentation of knee cartilages in MR images with artificial intelligence
. Arthroscopy 2012; 28: 1180–1183. 5 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278: 563
radiological image assessment is a subjective visual evaluation of medical images by radiologists. The traditional way of quantitative feature extraction is the strategy of radiomics: a region of interest (ROI) is manually selected, and different quantitative
-disease, microvascular dysfunction). Furthermore, spectral CT imaging might help detect true perfusion defects using iodine density reconstructions in the future. Also, radiomic and machine learning analysis of the left ventricle may help overcome the limitations of CTP
Access 2020 . [11] Wickramasinghe SU , Weerakoon TI , Gamage PJ , Bandara MS , Pallewatte A : Identification of radiomic features as an imaging marker to differentiate benign and malignant breast masses based on magnetic resonance imaging
. Qi Y , Zhao T , Han M . The application of radiomics
A hepatocellularis carcinoma komplex kezelése.
Konszenzuskonferencia, Budapest, 2021. április 24.
Complex management of hepatocellular carcinoma.
Consensus Conference, Budapest, April 24, 2021
Shan QY, Hu HT, Feng ST et al. Ct-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019; 19
