OBJECTIVES: This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cance...
BACKGROUND/OBJECTIVES: Calculating the radiation dose from CT in F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation dos...
Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; however, challenges persist regarding image quality and accessibility during NMI-based treatment. This paper reviews the use of deep learning methods for g...
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised ...
PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC...
RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma...
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Dec 3, 2024
Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. ...
European journal of nuclear medicine and molecular imaging
Dec 2, 2024
PURPOSE: The objective of this study is to generate reliable K parametric images from a shortened [F]FDG total-body PET for clinical applications using a self-supervised neural network algorithm.
OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.
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