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 …

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 …

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 …

The advancement of artificial intelligence concurrent with the development of medical
imaging techniques provided a unique opportunity to turn medical imaging from mostly …

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 …

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 …

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 …

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 …

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 …

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 …