Although a variety of imaging modalities are used or currently being investigated for patients
with brain tumors including brain metastases, clinical image interpretation to date uses only …

Purpose To investigate the feasibility of tumor type prediction with MRI radiomic image
features of different brain metastases in a multiclass machine learning approach for patients …

Simple Summary Non-invasive imaging modalities are commonly used in clinical practice.
Recently, the application of machine learning (ML) techniques has provided a new scope for …

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards
precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or …

Objective To examine the capability of MRI texture analysis to differentiate the primary site of
origin of brain metastases following a radiomics approach. Methods Sixty-seven untreated …

Radiomics has become an area of interest for tumor characterization in 18F-
Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG …

Purpose Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor
prognosis. Radiomic features have emerged as promising predictors of the tumor …

Background: Brain metastases are associated with poor survival. Molecular genetic testing
informs on targeted therapy and survival. The purpose of this study was to perform a MR …

Purpose More and more small brain metastases (BMs) in asymptomatic patients can be
detected even prior to their primary lung cancer with the development of MRI. The aim of this …

Background Morphological findings showed poor accuracy in differentiating
angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). Purpose To …