Radiology

Ultrasonographic Phenotypes and Segmentation Model Comparison for Primary Breast Lesions and Axillary Lymph Nodes in Triple-negative Breast Cancer.

RATIONALE AND OBJECTIVES: To describe clinicopathologic and ultrasonographic heterogeneity across immunohistochemistry-based FUSCC surrogate subtypes in triple-negative breast cancer (TNBC) and compare 3 automatic segmentation models for primary breast lesions and axillary lymph nodes (ALNs) on two-dimensional ultrasound. MATERIALS AND METHODS: This single-center retrospective study included 102 T...

AI-Assisted Prostate Cancer Diagnosis Using Biparametric MRI and PI-RADS v2.1: Performance Comparison Between Novice-Level and Experienced Readers.

BACKGROUND: Despite promising results of artificial intelligence (AI) in prostate cancer (PCa) detection, its impact on biparametric MRI (bpMRI) interpretation remains uncertain, especially for readers with limited experience. PURPOSE: To evaluate the effect of AI software assistance on prostate bpMRI interpretation by readers with different levels of prostate MRI experience. STUDY TYPE: Retrospec...

Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial.

OBJECTIVES: Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repe...

A multimodal MRI radiomics model for distinguishing borderline from malignant ovarian epithelial tumors.

BACKGROUND: To develop and validate a multimodal MRI radiomics machine learning model for differentiating borderline epithelial ovarian tumors (BEOTs)...

Multi-label dynamic diagnosis of pancreatic diseases using AI-enhanced endoscopic ultrasound: a multi-cohort real-world study.

BACKGROUND: Artificial intelligence (AI) is a promising tool for pancreatic disease diagnosis using Endoscopic Ultrasound (EUS) images. But, current m...

Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

Accurate prediction of disease-free survival (DFS) is essential for tailoring adjuvant regimens and improving clinical outcomes in early-stage breast ...

Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.

Accurate automated segmentation of Lumbar Spine Structures (LSS) in Magnetic Resonance Imaging (MRI) is important for effective diagnosis and treatmen...

Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study.

The peculiarities of older individuals related to osteoporosis and hyperostosis may lead to a higher rate of misdiagnosis of rib fractures on computed...

Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.

OBJECTIVES: To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year effic...

Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with t...

A Multifactor Prediction Model of Recovery of Consciousness in Patients With Unresponsive Wakefulness Syndrome: A Multicenter, Retrospective Study.

AIMS: This multicenter retrospective cohort study aimed to develop 3 predictive models to estimate consciousness status 3 months after admission. Thes...

Utility of Single-Shot Fast Spin-Echo T2-Weighted MRI With Deep Learning Reconstruction for Assessing Myometrial Invasion in Endometrial Cancer.

OBJECTIVE: To evaluate the clinical feasibility of single-shot fast spin-echo T2-weighted MRI with deep learning reconstruction (SSFSE-DL-T2WI) for as...

Advances in artificial intelligence for the evaluation of mitral regurgitation.

PURPOSE OF REVIEW: The diagnostic evaluation of mitral regurgitation (MR) is complex, time-intensive, and prone to significant interobserver variabili...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigastric veins on contrast-enhanced CT images in patie...

Abdominal Dual-Energy CT with Deep Learning Reconstruction: Preserving Image Quality and Hepatic Lesion Conspicuity with Reduced Radiation and Contrast.

The objective was to evaluate the image quality and hepatic lesion conspicuity in a dual-low-dose (radiation and contrast volume) upper abdominal dual...

Simulation-based training in cardiovascular intervention and cardiac surgery: bridging skill, safety, and innovation.

The increasing complexity of cardiovascular procedures, regulatory constraints, and heightened patient safety requirements have necessitated a fundame...

Navigating Uncertainty in MRI Diagnosis: A Human-AI Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer.

BACKGROUND: Deep learning (DL) methods have shown potential for predicting clinically significant prostate cancer (csPCa), but radiologists often face...

Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study.

BACKGROUND: As the histopathology workforce continues to struggle and service demand continues to increase, it has become prudent to consider viable a...

Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often diagnosed late, increasing avoidable risk and delaying treatment. Artificial intelligence (AI) ...

Neuroimaging studies of non-suicidal self-injury in young adults with major depressive disorder.

Non-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD), elevating suicide risk and indicating poor pr...