AIMC Topic: Fluorodeoxyglucose F18
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A deep learning model for generating [F]FDG PET Images from early-phase [F]Florbetapir and [F]Flutemetamol PET images.
European journal of nuclear medicine and molecular imaging
Jun 11, 2024
INTRODUCTION: Amyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunct...
Automated Lugano Metabolic Response Assessment in F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on F-Fluorodeoxyglucose-Positron Emission Tomography.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
Jun 6, 2024
PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.
Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Journal of applied clinical medical physics
May 29, 2024
PURPOSE: This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.
Whole Body Imaging
Positron Emission Tomography Computed Tomography
Radiopharmaceuticals
Image Interpretation, Computer-Assisted
Fluorodeoxyglucose F18
Signal-To-Noise Ratio
Middle Aged
Deep Learning
Image Processing, Computer-Assisted
Lymphoma
Humans
Prognosis
Female
Aged
Aged, 80 and over
Young Adult
Adult
Male
Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis.
Clinical radiology
May 27, 2024
AIM: The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features.
Preliminary study on the ability of the machine learning models based on F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma.
European journal of radiology
May 25, 2024
PURPOSE: The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati...
Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
Medical physics
May 6, 2024
BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughpu...
Prognostic value of combining clinical factors, F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.
Annals of nuclear medicine
May 5, 2024
OBJECTIVE: To investigate the prognostic value of F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma...
Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2024
We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. We extracted 1,037 F-FDG PET radiomic features q...
Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis.
Nuclear medicine communications
Apr 18, 2024
OBJECTIVE: FDG PET imaging plays a crucial role in the evaluation of demented patients by assessing regional cerebral glucose metabolism. In recent years, both radiomics and deep learning techniques have emerged as powerful tools for extracting valua...
Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.
Annals of nuclear medicine
Apr 8, 2024
OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.
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