Radiology

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-based retrospective pharmacokinetic quantification (RoQ) of early-treatment dynamic contrast-enhanced (DCE) MRI. Materials and Methods This multicenter retrospective study included breast MRI data from four publicly available datasets of patients wit...

MRI-based interpretable clinicoradiological and radiomics machine learning model for preoperative prediction of pituitary macroadenomas consistency: a dual-center study.

PURPOSE: To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively.

Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate car...

Deep learning-based automatic detection and grading of disk herniation in lumbar magnetic resonance images.

Magnetic resonance imaging of the lumbar spine is a key technique for clarifying the cause of disease. The greatest challenges today are the repetitiv...

Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images.

Diabetic retinopathy (DR) is an age-related macular degeneration eye disease problem that causes pathological changes in the retinal neural and vascul...

A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography.

BACKGROUND: In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality...

Development of a deep learning-based MRI diagnostic model for human Brucella spondylitis.

INTRODUCTION: Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and ...

Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.

PURPOSE: This study aimed to develop an early predictive model for neoadjuvant therapy (NAT) response in breast cancer by integrating multimodal ultra...

Differentiated thyroid cancer and positron emission computed tomography: when, how and why?

INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering ...

OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our stu...

Contemporary Concise Review 2024: New Techniques in Interventional Pulmonology.

Navigational bronchoscopy and cone beam computed tomography (CBCT) guided bronchoscopy show comparable yields to percutaneous transthoracic needle bio...

Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded im...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity de...

Corrigendum to "Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review" [Clin Imaging 118 (2025) 110369].

Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.

Foundation models (FMs) represent a significant evolution in artificial intelligence (AI), impacting diverse fields. Within radiology, this evolution ...

Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.

PURPOSE: The histological grade of hepatocellular carcinoma (HCC) is an important factor associated with early tumor recurrence and prognosis after su...

MTMedFormer: multi-task vision transformer for medical imaging with federated learning.

Deep learning has revolutionized medical imaging, improving tasks like image segmentation, detection, and classification, often surpassing human accur...

A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.

Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and provides critical insights through ultrasound, MR...

Development of a deep learning model for predicting skeletal muscle density from ultrasound data: a proof-of-concept study.

Reduced muscle mass and function are associated with increased morbidity, and mortality. Ultrasound, despite being cost-effective and portable, is sti...

Deep Learning Approach for Biomedical Image Classification.

Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcomes across diverse medical dis...