The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …

Background Cervical cancer is one of the most common cancers in women with an
incidence of around 6.5% of all the cancer in women worldwide. Early detection and …

Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could
help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a …

Objective To develop and validate a radiomics signature based on magnetic resonance
imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response …

Introduction Lymphovascular space invasion (LVSI) is associated with lymph node
metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential …

Objectives To compare computed tomography (CT)–based radiomics for preoperatively
differentiating type I and II epithelial ovarian cancers (EOCs) using different machine …

Background To investigate the magnetic resonance imaging (MRI)-based radiomics value in
predicting the survival of patients with locally advanced cervical squamous cell cancer …

Objective Radiomics is the process of extracting quantitative features from radiological
images, and represents a relatively new field in gynecological cancers. Cervical cancer has …

Radiomics is an emerging field of research that aims to find associations between
quantitative information extracted from imaging examinations and clinical data to support the …

Objective To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics
based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage …