AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).
AIMS: This study assessed the diagnostic accuracy and prognostic implications of an artificial intelligence (AI) tool for coronary artery calcification (CAC) assessment on nongated, noncontrast thoracic computed tomography (CT).
AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical stage IA non-small cell lung cancer.
UNLABELLED: The Radiology team from a large Breast Screening Unit in the UK with a screening population of over 135,000 took part in a service evaluation project using artificial intelligence (AI) for reading breast screening mammograms.
AIM: Prospective outcome prediction plays a crucial role in guiding preoperative decision-making in patients with Chiari malformation type I (CM-Ⅰ) with syringomyelia. Here, we aimed to develop a predictive model for postoperative outcomes in patient...
AIM: To develop and validate a machine learning (ML) model based on positron emission tomography/computed tomography (PET/CT) multi-modality fusion radiomics to improve the prediction efficiency of mediastinal-hilar lymph node metastasis (LNM).
AIM: Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying fie...
AIM: To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain-deep learning-reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.
AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.