Radiomics is a rapidly developing field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed
molecular studies have revealed marked intra-patient heterogeneity at the tumour …

Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be
reliably segmented on computed tomography (CT) using fully automated deep learning …

Despite implementing hundreds of strategies, cancer drug development suffers from a 95%
failure rate over 30 years, with only 30% of approved cancer drugs extending patient …

Simple Summary Predicting which patients will respond to therapy or experience disease
relapse can help clinicians select treatments that could slow down or prevent the spread of …

To evaluate giant aneurysms (GiAn) prevalence in Arab countries and examine contributing
factors; and to review Kawasaki disease (KD) publication trends and collaborations among …

High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and
temporal heterogeneity, typically manifesting at an advanced metastatic stage. A major …

La modélisation des résultats peut caractériser le comportement d'une réponse tissulaire à
un traitement qui est basé sur des données multi-omiques spécifiques au patient (par ex …

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide, characterized by
aggressive behavior, high relapse rate, and rapid progression. The cornerstone of OC …

Outcome modeling can characterize the behavior of tissue response to a treatment based on
patient-specific multi-omics data (eg clinical information, demographics, dosimetric profiles …