Radiomics in medical imaging—“how-to” guide and critical reflection
JE Van Timmeren, D Cester, S Tanadini-Lang… - Insights into …, 2020 - Springer
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the
existing data available to clinicians by means of advanced mathematical analysis. Through …
existing data available to clinicians by means of advanced mathematical analysis. Through …
Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods
Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …
quantitative features used for clinical decision support. However, these radiomic features are …
[HTML][HTML] Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI)
field. Modern radiation oncology is based on the exploitation of advanced computational …
field. Modern radiation oncology is based on the exploitation of advanced computational …
[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 …
[HTML][HTML] Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework
The advancement of artificial intelligence concurrent with the development of medical
imaging techniques provided a unique opportunity to turn medical imaging from mostly …
imaging techniques provided a unique opportunity to turn medical imaging from mostly …
Harmonization strategies for multicenter radiomics investigations
Carrying out large multicenter studies is one of the key goals to be achieved towards a faster
transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics …
transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
Structural and functional radiomics for lung cancer
Introduction Lung cancer ranks second in new cancer cases and first in cancer-related
deaths worldwide. Precision medicine is working on altering treatment approaches and …
deaths worldwide. Precision medicine is working on altering treatment approaches and …
[HTML][HTML] Machine learning applications in radiation oncology
Abstract Machine learning technology has a growing impact on radiation oncology with an
increasing presence in research and industry. The prevalence of diverse data including 3D …
increasing presence in research and industry. The prevalence of diverse data including 3D …
Robustness of radiomic features in magnetic resonance imaging: review and a phantom study
R Cattell, S Chen, C Huang - … computing for industry, biomedicine, and art, 2019 - Springer
Radiomic analysis has exponentially increased the amount of quantitative data that can be
extracted from a single image. These imaging biomarkers can aid in the generation of …
extracted from a single image. These imaging biomarkers can aid in the generation of …