Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.

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.

Methods: A total of 123 cases devoid of lymphoma underwent whole-body 18F-FDG-PET/CT scans to facilitate the development of an advanced SAU2Net model, which combines the advantages of U2Net and attention mechanism. This model integrated inputs from simulated 1/2-dose (0.

A nomogram based on F-fluorodeoxyglucose PET/CT and clinical features to predict epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.

Background: Identifying epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LADC) is vital for treatment decision-making. This study aimed to establish a convenient and noninvasive nomogram prediction model based on F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) imaging and clinical features to predict EGFR mutation status in patients with LADC.

Methods: A total of 274 patients (male 130, female 144, median age 65 years) were enrolled in this retrospective study.

The diagnostic and predictive efficacy of F-FDG PET/CT metabolic parameters for EGFR mutation status in non-small-cell lung cancer: A meta-analysis.

Purpose: To investigate the predictive performance of the maximum standardized uptake value (SUV) and mean standardized uptake value (SUV) of primary lesions based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) for EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

Methods: The PubMed/Medline, Embase, Cochrane Library and Web of Science databases were searched as of January 1, 2021. Studies whose reported data could be used to construct contingency tables were included.