PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC...
RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma...
RATIONALE AND OBJECTIVES: Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descripti...
BACKGROUND/AIM: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is...
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
RATIONALE AND OBJECTIVES: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of i...
PURPOSE: To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation.
RATIONALE AND OBJECTIVES: Metastatic bone tumors significantly reduce patients' quality of life and expedite cancer spread. Traditional diagnostic methods rely on time-consuming manual annotations by radiologists, which are prone to subjectivity. Emp...
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (...
RATIONALE AND OBJECTIVES: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to tra...