Radiology

A hybrid deep learning framework for accurate breast cancer classification using MRI images.

Early and accurate detection of breast cancer is crucial to enhance patient results, especially in high-risk populations where magnetic resonance imaging (MRI) is intensively used. This study shows a deep learning-based framework for the automatic classification of benign and malignant breast lesions using MRI images. To guarantee accurate patient-level annotations, a large-scale dataset comprisin...

Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.

BACKGROUND: Estrogen receptor (ER) expression is a key prognostic and predictive marker in breast cancer. The 2020 ASCO/CAP guidelines classify tumors with ≥ 1% ER-positive nuclei as ER-positive, yet ER-low positive (1%-10%) breast cancer remains biologically distinct with an unclear response to endocrine therapy (ET). Given the limitations of invasive ER assessment, we developed and validated an ...

Diagnostic performance of artificial intelligence in fracture detection with ultrasound: A systematic review and meta-analysis.

BACKGROUND: Bone fractures are common in acute care, and point-of-care ultrasound (POCUS) is an emerging diagnostic tool that can be complementary to ...

Precise Staging of Diabetic Retinopathy Through Machine Learning Analysis of Leakage Source Characteristics: A Radiomics-Driven Approach Integrating OCT-A and FFA.

INTRODUCTION: Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients...

A Novel 2.5-Dimensional Deep Learning Model for "Bone-on-Bone" Detection on Magnetic Resonance Imaging in Medial Unicompartmental Knee Arthroplasty Candidates.

BACKGROUND: Accurate identification of "bone-on-bone" (BoB) osteoarthritis is critical for patient selection for medial unicompartmental knee arthropl...

A hybrid CNN-GCN framework for interpretable Alzheimer's disease diagnosis from MRI scans.

Medical image analysis for Alzheimer's Disease (AD) diagnosis faces two key challenges: capturing spatial dependencies between anatomically connected ...

Fully automated artificial intelligence-based echocardiographic analysis substantially reduces workflow time while preserving measurement accuracy: a pilot study.

BACKGROUND: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measurements and qualitative visual assessment. F...

Deep Learning Analysis of Indocyanine Green Fluoroscopy of Ureters in Robotic Cystectomy: Toward Reducing Ureteroenteric Strictures.

OBJECTIVES: To develop a deep learning method to quantify ureter perfusion during indocyanine green (ICG) fluoroscopy in robot-assisted radical cystec...

Development and Validation of a Deep Learning-enabled Single Breath-hold Abbreviated MRI Protocol for Hepatocellular Carcinoma Diagnosis.

Purpose To develop a deep learning-enabled single breath-hold abbreviated MRI (DL-SBH-aMRI) protocol for hepatocellular carcinoma (HCC) diagnosis. Mat...

Tumor-preserving Deformable Registration of Longitudinal Breast DCE MRI during Neoadjuvant Chemotherapy.

Purpose To develop a deep learning-based deformable registration method for breast dynamic contrast-enhanced (DCE) MRI that preserves tumor regions wh...

A hybrid radiomics framework integrating genetic algorithm-optimized random forest for preoperative identification of Luminal B breast cancer and Ki-67 prediction: A multicenter study.

BACKGROUND: Preoperative identification of Luminal B breast cancer remains a clinical challenge. This study aimed to develop an ultrasound radiomics f...

Accuracy and precision of automated kidney stone detection on CT.

BACKGROUND: While computed tomography (CT) is the preferred imaging modality for kidney stone detection and measurement, quantifying the accuracy and ...

Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI.

OBJECTIVE: Tumor budding (TB) is a histopathological marker of aggressive behavior and poor prognosis in rectal cancer (RC), yet not reliably evaluate...

MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.

OBJECTIVES: To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contras...

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...

Predicting resistance to neoadjuvant chemotherapy in osteosarcoma using machine learning with clinical data and T2-weighted MRI radiomics.

OBJECTIVES: Identifying patients at risk of chemoresistant osteosarcoma enables risk-adapted management. This study aimed to predict chemoresistant os...

Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank.

Cognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict i...

Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction-Related Cardiogenic Shock: Least Absolute Shrinkage and Selection Operator Nomogram Development and Validation.

BACKGROUND: Acute kidney injury critically impacts outcomes in cardiogenic shock secondary to acute myocardial infarction (CS-AMI). Acute kidney injur...

Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.

BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their ...

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and ...