Radiology

Brain Tumor Classification and Severity Identification Using Deep Convolutional Spiking U-Net Lyrebird Neural Network and Alpha Piecewise Linear-Fuzzy.

Purpose: To develop a robust framework that accurately classifies brain tumors and provides an estimation of their severity using an artificial intelligence approach to solve issues related to multimodal MRIs (Magnetic Resonance Imaging), such as resolution variability, misalignment and heterogeneity.Methodology: An intelligent Deep Convolutional Spiking U-Net Lyrebird Neural Network combined with...

Photon-counting Detector CT Spectral Reconstructions for Radiomics-based Liver Lesion Classification: A Multicenter Study.

OBJECTIVES: Accurate noninvasive classification of hepatic lesions remains a diagnostic challenge, particularly on conventional CT. Photon Counting Detector CT (PCD-CT) offers spectral imaging capabilities that may enhance tissue characterization. This study aimed to evaluate the performance of radiomics-based machine learning for differentiating benign and malignant liver lesions using multispect...

Transcriptional signatures and topological reorganization of morphometric similarity networks in temporal lobe epilepsy with unilateral hippocampal sclerosis.

OBJECTIVE: To delineate morphometric similarity network (MSN) topological abnormalities and their underlying spatial transcriptomics in the normal-app...

European Society of Urogenital Radiology (ESUR) perspectives on the role of prostate MRI in active surveillance.

MRI has a central role in the diagnosis and management of prostate cancer, including active surveillance (AS) of low- and favourable intermediate-risk...

Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI.

OBJECTIVES: Motion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. ...

Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study.

OBJECTIVES: To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comp...

Advancing diagnostic biomarkers in Alzheimer's disease: interdisciplinary innovations and technological frontiers.

Developing diagnostic biomarkers for Alzheimer's disease (AD) is at the cutting edge of interdisciplinary research and technical advancement. This com...

Paradigm shift in medicine and in clinical reasoning method: time has come for a new chest semiotics.

INTRODUCTION: Clinical reasoning in medicine is a complex cognitive process that integrates sensory perception, interpretation, and abductive inferenc...

Current Patient Attitudes to Artificial Intelligence Applications in Radiology.

OBJECTIVES: Healthcare systems are now funding implementation of artificial intelligence (AI) algorithms in radiology, which will change the experienc...

Dosimetry Analysis of 177Lu-PSMA-I&T in Patients with Low-Volume Oligometastatic Hormone-Sensitive Prostate Cancer: A Secondary Analysis of the LUNAR Trial.

The phase 2 LUNAR trial randomized (1:1) patients with oligorecurrent hormone-sensitive prostate cancer to neoadjuvant [177Lu]Lu-PSMA-I&T (2 cycles, 6...

Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.

OBJECTIVES: To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the effi...

Analyzing multiple-sclerosis progression: stage-specific biomarker insights via explainable machine learning.

BACKGROUND: Multiple Sclerosis (MS) is a chronic autoimmune disease where early diagnosis from Clinically Isolated Syndrome (CIS) remains challenging....

Automated Detection and Classification of Pleural Effusion on Computed Tomography Using Deep Learning.

The aim of this study is to develop and evaluate the performance of a two-stage deep learning-based artificial intelligence framework for the automati...

Contrast-Enhanced VM-UNet for Thyroid Nodule Segmentation Based on Wavelet Features and Dual-Branch Edge Learning.

Accurate segmentation of thyroid nodules from ultrasound images is critical for clinical diagnosis and treatment, as manual analysis is labor-intensiv...

Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated Modeling of Batch Effects and Clinical Covariates.

This study proposes a Residual Conditional Variational Autoencoder model (ResCVAE-Harmonizer) that integrates batch information and clinical covariate...

Automated Detection and Classification of Radiology Report Discrepancies Using NLP: A Tool for Resident Education and Quality Assurance.

PURPOSE: The aim of this study was to develop and evaluate a natural language processing (NLP) system that automatically detects and classifies discre...

Real-world text-only inference of PI-RADS v2.1 from prostate MRI reports using large language models: a lesion-level, zone-aware study.

PURPOSE: To evaluate the feasibility and limitations of real-world, text-only inference of PI-RADS v2.1 categories from prostate MRI reports using lar...

Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.

Magnetic resonance imaging (MRI) has greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different...

UniPET: A universal network for high-quality PET image denoising across varied dose reduction factors.

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, t...

SCAR-Net-assisted ultrasound diagnosis of postoperative scars and recurrent lesions in breast cancer.

Postoperative differentiation between scar tissue and recurrent lesions in patients with breast cancer presents a significant diagnostic challenge. Th...