Introduction to radiomics
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
[HTML][HTML] Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
Medical image processing and analysis (also known as Radiomics) is a rapidly growing
discipline that maps digital medical images into quantitative data, with the end goal of …
discipline that maps digital medical images into quantitative data, with the end goal of …
Radiomics in oncology: a practical guide
Radiomics refers to the extraction of mineable data from medical imaging and has been
applied within oncology to improve diagnosis, prognostication, and clinical decision support …
applied within oncology to improve diagnosis, prognostication, and clinical decision support …
Coronavirus (covid-19) classification using ct images by machine learning methods
This study presents early phase detection of Coronavirus (COVID-19), which is named by
World Health Organization (WHO), by machine learning methods. The detection process …
World Health Organization (WHO), by machine learning methods. The detection process …
Image biomarker standardisation initiative
The image biomarker standardisation initiative (IBSI) is an independent international
collaboration which works towards standardising the extraction of image biomarkers from …
collaboration which works towards standardising the extraction of image biomarkers from …
Interpretation of radiomics features–A pictorial review
AA Ardakani, NJ Bureau, EJ Ciaccio… - Computer methods and …, 2022 - Elsevier
Radiomics is a newcomer field that has opened new windows for precision medicine. It is
related to extraction of a large number of quantitative features from medical images, which …
related to extraction of a large number of quantitative features from medical images, which …
[HTML][HTML] Radiomics and artificial intelligence in lung cancer screening
F Binczyk, W Prazuch, P Bozek… - Translational lung cancer …, 2021 - ncbi.nlm.nih.gov
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …
million associated deaths reported in 2018. The key issue in the fight against this disease is …
18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma
JJ Eertink, T van de Brug, SE Wiegers… - European journal of …, 2022 - Springer
Purpose Accurate prognostic markers are urgently needed to identify diffuse large B-Cell
lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to …
lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to …
A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling
Radiomics applies machine learning algorithms to quantitative imaging data to characterise
the tumour phenotype and predict clinical outcome. For the development of radiomics risk …
the tumour phenotype and predict clinical outcome. For the development of radiomics risk …
Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
H Moradmand, SMR Aghamiri… - Journal of applied …, 2020 - Wiley Online Library
To investigate the effect of image preprocessing, in respect to intensity inhomogeneity
correction and noise filtering, on the robustness and reproducibility of the radiomics features …
correction and noise filtering, on the robustness and reproducibility of the radiomics features …