Introduction: In this review we describe recent developments in the field of radiomics along
with current relevant literature linking it to tumor biology. We furthermore explore the …

With the genomic revolution in the early 1990s, medical research has been driven to study
the basis of human disease on a genomic level and to devise precise cancer therapies …

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

Purpose To investigate the value of T2-weighted–based radiomics compared with
qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for …

We aimed to identify optimal machine-learning methods for radiomics-based prediction of
local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled …

Background Radiogenomics is the extension of radiomics through the combination of
genetic and radiomic data. Because genetic testing remains expensive, invasive, and time …

Background To develop an artificial intelligence (AI) model with radiomics and deep
learning (DL) features extracted from CT images to distinguish benign from malignant …

Artificial intelligence (AI) is currently regaining enormous interest due to the success of
machine learning (ML), and in particular deep learning (DL). Image analysis, and thus …

Rationale and Objectives To evaluate an MRI radiomics-powered machine learning (ML)
model's performance for the identification of deep myometrial invasion (DMI) in endometrial …

Autologous T cells genetically engineered to express chimeric antigen receptor (CAR) have
shown promising outcomes and emerged as a new curative option for hematological …