Journal of cancer research and clinical oncology
Feb 5, 2024
PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathologica...
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 3, 2024
OBJECTIVE: This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue.
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imagi...
INTRODUCTION: In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, whi...
OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with o...
RATIONALE AND OBJECTIVES: To explore the classification and prediction efficacy of the deep learning model for benign prostate lesions, non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) in Prost...
PURPOSE: The purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T...
BACKGROUND: Modic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities.
OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA).
INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion therapies. Conventional high-field (HF) MRI yields the highest accuracy in discriminating early ischaemia from haemorrhages and mimics. Rapid access t...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.