Nuclear Medicine

[18F]FDG-PET in Lung Cancer: From Staging to Therapy Response.

Lung cancer, the most commonly diagnosed malignancy, is the leading cause of cancer-related mortality worldwide. Advancements in molecular imaging have expanded the role of [18F] 2-Fluoro-2-deoxy-glucose (FDG) PET/CT in the management of lung cancer, from the evaluation of pulmonary nodules (including solitary pulmonary nodule) to staging, radiotherapy planning and response evaluation. This review...

Machine Learning Based on Multiparametric Features from Dual-Layer Detector Spectral CT for Identifying Ulcer-Like Projection in Aortic Intramural Hematoma.

Aortic intramural hematoma (IMH) is a high-risk subtype of acute aortic syndrome (AAS). The presence of ulcer-like projection (ULP) significantly increases the risk of progression to dissection, aneurysm, or rupture. However, conventional computed tomography angiography (CTA) has limited reliability in differentiating small ULP from the surrounding high-density hematoma or coexisting atherosclerot...

Hidden risk in normal myocardial perfusion scans: AI-detected proximal coronary calcium on CT attenuation maps improves prognosis.

PURPOSE: Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial...

Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.

OBJECTIVES: This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tu...

Diagnostic performance of ¹⁸F-FDG PET/CT radiomics-based machine learning models for pancreatic lesion characterisation: Comparison with visual assessment and evaluation of human-radiomics synergy.

PURPOSE: We aimed to evaluate the diagnostic performance of ¹⁸F-FDG PET/CT-derived radiomics-based machine learning models for differentiating maligna...

Current Treatment Options and Emerging Technologies for Pelvic Bone Tumors: From Limb Salvage to Precision Oncology.

In our practice, the treatment of pelvic bone tumors should begin with a multidisciplinary sarcoma-board evaluation that integrates biopsy-proven hist...

High FDG Uptake in the Non-cancerous Lung Predicts Immune-Related Adverse Events and Poor Prognosis in Patients with Lung Cancer Treated with Immune Checkpoint Inhibitors.

RATIONALE AND OBJECTIVES: Immune checkpoint inhibitors (ICIs) have improved survival in non-small cell lung cancer (NSCLC); however, predicting immune...

Arterial Spin Labeling of Cerebral Perfusion in Unresponsive Wakefulness Syndrome: A Proof-of-Concept Comparison with FDG-PET.

BACKGROUND: Despite the clinical utility of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) in evaluating patients with unresponsive w...

The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [18F]FDG PET/CT segmentation approach.

PURPOSE: To develop a deep learning (DL) approach for automatic segmentation and accurate risk stratification in multiple myeloma (MM) using whole-bod...

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-123I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.

BACKGROUND: Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning...

PET/CT-derived whole-body composition and survival in resectable NSCLC: double machine learning-based adjusted association analysis of intermuscular adiposity burden and sex-specific metabolic phenotypes.

PURPOSE: To evaluate whether whole-body PET/CT-derived body composition features are associated with survival in patients with resectable non-small ce...

Beyond FDG: a paradigm shift in precision localization of epileptogenic zones in refractory epilepsy using multimodal molecular imaging (PET/CT) and isotropic 3D MRI fusion.

Surgical resection for drug-resistant focal epilepsy relies on the precise presurgical localization of the epileptogenic zone (EZ). Although [1⁸F]FDG-...

MRI-free amyloid PET quantification using a deep learning model and white matter reference.

BackgroundAccurate quantification of standardized uptake value ratio (SUVR) in amyloid PET is essential for Alzheimer's disease (AD) diagnosis but typ...

A Blueprint for Agentic Workflow Support in Dynamic SPECT Myocardial Perfusion Imaging.

Automated identification of cardiac amyloidosis using cross-modal neural networks on [Formula: see text]-Pyrophosphate SPECT imaging and clinical data.

Accurate, early identification of transthyretin cardiac amyloidosis (ATTR-CA) is challenging yet critical for effective treatment. In this work, the m...

Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis.

PURPOSE: Gastric-type endocervical adenocarcinoma (GAS) is an aggressive, non-HPV-associated cervical adenocarcinoma that is often difficult to recogn...

Recent advances in radioisotope geochronology for quaternary environmental and paleoclimate reconstruction.

Reliable chronological frameworks are fundamental for reconstructing Quaternary environmental change, paleoclimate evolution, landscape development, a...

EM-AWFNet: dual-branch adaptive weight fusion for lung cancer PET/CT classification.

Lung cancer (LC) is a malignant tumor that poses a serious threat to human health. Positron emission tomography/computed tomography (PET/CT) imaging, ...

Can AI Make Paediatric PET/CT Lower Dose? Opportunities, Evidence and Cautions.

Artificial intelligence (AI) has emerged as a potential enabling technology for lower-dose paediatric PET/CT, but its value depends on whether dose re...

Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.

BACKGROUND: Distinguishing individuals with cognitive decline (CD), including early Alzheimer's disease, from cognitively normal (CN) individuals is e...