Radiology

Assessing the ability of large language models to simplify lumbar spine imaging reports into patient-facing text: a pilot study of GPT-4.

OBJECTIVE: To assess the ability of large language models (LLMs) to accurately simplify lumbar spine magnetic resonance imaging (MRI) reports. MATERIALS AND METHODS: Patients who underwent lumbar decompression and/or fusion surgery in 2022 at one tertiary academic medical center were queried using appropriate CPT codes. We then identified all patients with a preoperative ICD diagnosis of lumbar sp...

Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.

BACKGROUND: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer. METHODS: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonanc...

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.

OBJECTIVE: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics ...

AI-Driven Fetal Liver Echotexture Analysis: A New Frontier in Predicting Neonatal Insulin Imbalance.

OBJECTIVES: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepa...

Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.

PURPOSE: Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and a...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

Role of Multimodality Imaging in the Evaluation and Management of Cardiomyopathy and Heart Failure.

Cardiomyopathies and heart failure (HF) represent a diverse group of cardiac conditions that significantly impact global health. The increasing comple...

Enhancing Breast Density Assessment in Mammograms Through Artificial Intelligence.

Breast cancer is the leading cause of cancer-related deaths among women worldwide. Early detection through mammography significantly improves outcomes...

Prostate MR image segmentation using a multi-stage network approach.

Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 202...

Accelerated Patient-specific Non-Cartesian Magnetic Resonance Imaging Reconstruction Using Implicit Neural Representations.

PURPOSE: Accelerating magnetic resonance acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been develope...

Edge-Centric Brain Connectome Representations Reveal Increased Brain Functional Diversity of Reward Circuit in Patients With Major Depressive Disorder.

BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previ...

MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.

BACKGROUND: The primary aim of this research was to create and rigorously assess a deep learning radiomics (DLR) framework utilizing magnetic resonanc...

Mask-Guided and Fidelity-Constrained Deep Learning Model for Accurate Translation of Brain CT Images to Diffusion MRI Images in Acute Stroke Patients.

The early and precise diagnosis of stroke plays an important role in its treatment planning. Computed Tomography (CT) is utilised as a first diagnosti...

A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.

Deep progressive learning reconstruction (DPR) is a novel deep learning-based algorithm for PET imaging, yet its impact on quantitative metrics and ra...

Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.

High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions...

Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules.

PURPOSE: This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a pa...

[Coronary heart disease-a German paradox : Using AI-based technology to solve a relevant healthcare problem].

The German healthcare system is facing challenges in diagnosing coronary artery disease (CAD). These include high mortality rates, even when advanced ...

Detection and quantification of fluorescein angiography leakage: From manual grading to advances in machine learning.

Fluorescein angiography (FA) has long been a cornerstone for evaluating retinal vascular leakage in diseases like uveitis, diabetic retinopathy, and m...

Multi-regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer.

Non-invasive and precise identification of clinically significant prostate cancer (csPCa) is essential for the management of prostatic diseases. Our s...

Central venous catheters tip positioning: Insights from a retrospective analysis.

BACKGROUND: Tunneled central venous catheters (tCVCs) are essential in hemodialysis when arteriovenous access is not feasible. Their correct positioni...