Objective Machine learning, deep learning, and artificial intelligence (AI) are terms that have
made their way into nearly all areas of medicine. In the case of medical imaging, these …

Objectives We aimed to present the state of the art of CT-and MRI-based radiomics in the
context of ovarian cancer (OC), with a focus on the methodological quality of these studies …

Importance Epithelial ovarian carcinoma is heterogeneous and classified according to the
World Health Organization Tumour Classification, which is based on histologic features and …

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

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

Objective To evaluate the methodological rigor of radiomics-based studies using
noninvasive imaging in ovarian setting. Methods Multiple medical literature archives …

Objectives This study aims to evaluate the diagnostic performance of machine-learning-
based contrast-enhanced CT radiomic analysis for categorizing benign and malignant …

Texture analysis derived from computed tomography (CT) might be able to provide clinically
relevant imaging biomarkers and might be associated with histopathological features in …

AIM To construct and validate a computed tomography (CT)-based radiomics nomogram
integrating radiomics signature and clinical factors to distinguish ovarian cystadenomas and …

Background Radiomics analysis could provide complementary tissue characterization in
ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time …