Radiomics is an emerging field in quantitative imaging that uses advanced imaging features
to objectively and quantitatively describe tumour phenotypes. Radiomic features have …

Importance Advances in genomics have led to the recognition that tumors are populated by
distinct genotypic subgroups that drive tumor development and progression. The spatial and …

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response
to treatment. Known prognostic factors for this disease include homologous recombination …

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use
of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on …

Gliomas belong to a group of central nervous system tumors, and consist of various sub-
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …

Purpose Many radiomics features were originally developed for non‐medical imaging
applications and therefore original assumptions may need to be reexamined. In this study …

This study aims at developing a joint FDG-PET and MRI texture-based model for the early
evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the …

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …

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 …

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 …