Nuclear Medicine

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Transarterial radioembolization (TARE) is increasingly used for patients with hepatocellular carcinoma (HCC) across Barcelona Clinic Liver Cancer stages. It provides local disease control and downstaging or bridging to definitive therapy in selected cases. Imaging plays a central role in patient selection, dosimetry planning, and post-treatment assessment. This review aim...

Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.

OBJECTIVE: To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features for predicting RAS mutation status and prognosis in patients with colorectal cancer (CRC). METHODS: This retrospective study included 90 patients (mean age, 64 years, 60 men) with CRC who underwent pretreatment 18F-FDG-PET/CT. Radiomic features were...

Carotid inflammation on FDG-PET is associated with lower cognitive performance in people with treated HIV infection.

OBJECTIVES: Carotid atherosclerosis is an established risk factor for cognitive impairment. FDG-PET detects subtle inflammatory changes in the arteria...

Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson's disease and atypical parkinsonian syndrome.

PURPOSE: To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonis...

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.

BACKGROUND: Vessels encapsulating tumor clusters (VETCs), a CD34-positive vascular pattern in hepatocellular carcinoma (HCC), are linked to aggressive...

AI-augmented thyroid scintigraphy for robust classification of disease.

Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imbalanced d...

Innovation districts and transformative workspaces: A scoping review of AI-pet robots companionship for aging employees balancing productivity and wellbeing.

BACKGROUND: Considering the future of work and an aging workforce, emerging technologies such as artificial intelligence (AI) and robots are promising...

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Positron Emission Tomography (PET) diagnostic precision is often compromised by low spatial resolution. Deep learning restoration models tend to sacri...

Optimization of hypovascular liver lesion detectability in dual-energy CT using deep learning image reconstruction: a phantom study for potential iodine dose reduction.

OBJECTIVE: To determine the optimal low-keV level using deep learning image reconstruction (DLIR) that maximizes lesion detectability, and to assess t...

Machine learning-guided discovery of poly(ethylene terephthalate)-binding modules to enhance durable whole-cell degradation.

The enzymatic degradation of poly(ethylene terephthalate) (PET) offers a sustainable route for plastic recycling but is often hindered by limited enzy...

Automatic lesion segmentation in ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT: added value of PET-guided SPECT in a bicentric study.

BACKGROUND: Manual segmentation of prostate cancer metastases on PSMA PET/CT and SPECT/CT is time-consuming and poorly scalable, particularly in highl...

PET/MR: history, current state, and technology.

Integrated PET/MR combines the molecular sensitivity of PET with the superior soft-tissue contrast and multiparametric capabilities of MRI, enabling s...

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

Simultaneous dual-tracer PET provides more comprehensive information for clinical diagnosis than standard PET imaging, but separating the hybrid dual-...

Beyond the tumor: Recurrence-prone radiomics for prognostication in negative PSMA PET/CT scans of prostate cancer.

Prostate-specific membrane antigen (PSMA) PET/CT is routinely used to restage prostate cancer (PCa) in patients with biochemical recurrence (BCR), yet...

Secondary prevention of recurrent ischemic stroke: from guidelines to precision medicine.

Recurrent ischemic stroke remains a major global health challenge, accounting for substantial disability and mortality despite advances in acute manag...

An attention-guided multimodal deep learning framework by integrating CT-PET imaging and clinical data for lung cancer detection.

The proposed multi-modal deep learning system for lung cancer diagnosis and characterisation uses structural (CT), functional (PET), and clinical (EHR...

Nonlinear hydrothermal associations between coupled landscape ecological risk and resilience in a major grain-producing region of China.

In major grain-producing regions, hydrothermal processes are critical for regulating ecosystem stability. However, the long-term coupled dynamics betw...

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...

Integrating machine learning with histopathological, immunohistochemical, and PET parameters in the study of invasive breast carcinoma, no special type.

BACKGROUND: This study investigates the relationship between histopathological (HP) features, immunohistochemical (IHC) markers, 18F- FDG PET/CT param...

The optimal SVM model from multi-model comparison outperforms conventional criteria in preoperative prediction of ovarian cancer residual disease.

OBJECTIVE: To develop an interpretable artificial intelligence (AI)-based machine learning model integrating 18F-fluorodeoxyglucose positron emission ...