Simple Summary Ovarian cancer has a heterogeneous response to treatment, and relapse
may vary considerably. Different studies investigated the role of radiomics in ovarian cancer …

Objectives Recent studies have shown that deep learning based on pre-treatment positron
emission tomography (PET) or computed tomography (CT) is promising for distant …

Radiomic model reliability is a central premise for its clinical translation. Presently, it is
assessed using test–retest or external data, which, unfortunately, is often scarce in reality …

Background and purpose Magnetic resonance imaging guided radiotherapy (MRgRT) with
deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement …

In the era of internet of things and Industry 4.0, smart products and manufacturing systems
emit signals tracking their operating condition in real-time. Survival analysis shows its …

Abstract Background and Objective Recently, deep learning (DL) algorithms showed to be
promising in predicting outcomes such as distant metastasis-free survival (DMFS) and …

Over the last decades, the field of medicine has witnessed significant progress in artificial
intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems …

Simple Summary Machine learning based radiomics models for prediction of loco-regional
recurrence today mostly rely on features extracted from pre-treatment imaging data. In this …

Ovarian cancer (OC) is one of the most common types of cancer in women. Surgery and
chemotherapy are still the most common forms of treatment; however, their success depends …

Objectives To generate and validate state-of-the-art radiomics models for prediction of
radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) …