Radiology

Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging.

In clinical medicine, variables like disease severity are often categorized into discrete ordinal labels such as normal/mild/moderate/severe. However, these labels, commonly used to train and evaluate disease severity prediction models, simplify an underlying continuous severity spectrum. Using continuous scores can aid in detecting small severity changes more sensitively over time. We introduce a...

Large Language Models for the Differentiation of Benign and Malignant Liver Nodules based on Multimodal Prompts in Liver US Cases.

OBJECTIVE: Large language models (LLMs) that can process both images and text are increasingly being used in radiology. This study aimed to evaluate the performance of LLMs including GPT-4 Omni (GPT-4o), Claude-3.5-Sonnet (Claude), and Gemini 1.5 Pro (Gemini) in differentiating benign and malignant nodules in liver US cases and compare it with that of human readers. METHODS: Four hundred liver US ...

Predictive Model for Mild Autonomous Cortisol Secretion in Patients Evaluated in Artificial Intelligence-Driven Adrenal Incidentaloma Clinic.

BACKGROUND: Mild autonomous cortisol secretion (MACS) is present in approximately 20% to 50% of adrenal incidentalomas. These patients do not exhibit ...

[The use of artificial intelligence tools in the perception of electroradiologists].

BACKGROUND: Artificial intelligence (AI) is playing an increasingly important role in diagnostic imaging, helping specialists improve the quality and ...

Spatiotemporal Deep Learning for Scar Screening in CMR: Toward Selective Use of Gadolinium.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for assessing myocardial scar. However, a s...

Changes in computed tomography derived muscle and pulmonary tissue in patients hospitalized for COVID-19: a 12-month follow-up study.

BACKGROUND: Prolonged muscle loss and persistent pulmonary radiological manifestations have been observed among previously hospitalized COVID-19 patie...

Effect of Deep Learning Attenuation Correction on Accuracy of Stress Myocardial Perfusion Imaging.

BACKGROUND: Attenuation correction (AC) improves the accuracy of myocardial perfusion imaging for detecting coronary artery disease (CAD). There has b...

Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.

BACKGROUND: Mantle cell lymphoma (MCL) is a rare, biologically heterogeneous B-cell malignancy with highly variable outcomes. Existing prognostic tool...

Non-invasive diagnosis strategy integrating PSMA PET/CT and mpMRI for patients with suspected prostate cancer: a multi-center study.

BACKGROUND: Multiparametric MRI (mpMRI) and ^68 Ga-PSMA PET/CT are widely used for prostate cancer (PCa) diagnosis but remain limited by false positiv...

Incorporating normal periventricular changes for enhanced pathological white matter hyperintensity segmentation: on multiclass deep learning approaches.

White matter hyperintensities (WMH) detected on FLAIR MRI sequences serve as important biomarkers for cerebrovascular pathology, correlating with incr...

Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Axillary lymph node metastasis (ALNM) is an important factor in detecting breast cancer (BC). However, the noninvasive diagnosis of ALNM r...

Diagnostic Accuracy of Ultrasound Radiomics for Cervical Lymph-Node Metastasis in Papillary Thyroid Carcinoma: Evidence Predominantly From Chinese Cohorts.

Pre-operative identification of cervical lymph-node metastasis (LNM) guides surgical extent in papillary thyroid carcinoma (PTC) but remains imperfect...

Deep learning-guided discovery and engineering of binding peptides for accelerated enzymatic degradation of polyethylene terephthalate.

Enzymatic polyethylene terephthalate (PET) degradation holds promise for environmental restoration. However, limited substrate catalytic capacity hind...

Noninvasive Prediction of Bone Metastasis-Free Survival in Lung Adenocarcinoma Using Interpretable CT-based Deep Learning Model.

RATIONALE AND OBJECTIVES: Preoperatively identifying patients at high risk of bone metastasis (BM) remains challenging in resectable lung adenocarcino...

Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?

BACKGROUND: In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoin...

Multimodal imaging in left main and bifurcation revascularization: contemporary evidence and comparative modalities review.

Left main (LM) and bifurcation coronary artery disease (CAD) are anatomically complex lesions which require detailed planning and precise execution fo...

Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.

BACKGROUND: Breast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomogr...

Machine Learning-Based Synthetic Computed Tomography Derived From Temporal Bone Magnetic Resonance Imaging.

IMPORTANCE: For diagnostics and presurgical planning in otology, both magnetic resonance imaging (MRI) and computed tomography (CT) are frequently req...

Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial.

INTRODUCTION: Liver tumours are a leading cause of global morbidity and mortality. Current diagnostic tools, including computed tomography (CT), magne...

Circulating extracellular vesicle long RNA profiling combined with machine learning unveils novel diagnostic signature and molecular features in chronic pancreatitis.

BACKGROUND: No clinically useful non-invasive biomarkers have been developed for diagnosis of chronic pancreatitis (CP), and molecular features of CP ...