AIMC Topic: Fluorodeoxyglucose F18
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An FDG-PET-Based Machine Learning Framework to Support Neurologic Decision-Making in Alzheimer Disease and Related Disorders.
Neurology
Jun 27, 2025
BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex ...
A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.
BMC cancer
Jun 5, 2025
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...
Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.
Scientific reports
May 17, 2025
The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for inc...
The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
May 1, 2025
This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [F]FDG and [Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. We used 489 ...
F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.
BMC medical imaging
Apr 28, 2025
BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).
Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.
Clinical radiology
Apr 2, 2025
AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival...
Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.
Annals of nuclear medicine
Mar 29, 2025
OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marrow involvement (BMI) in patients with lymphoma.
Artificial intelligence for tumor [F]FDG-PET imaging: Advancement and future trends-part I.
Seminars in nuclear medicine
Mar 29, 2025
The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from various imaging modalities, including [F]FDG PET/CT, a well-established cornerstone of oncological imag...
F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.
Journal of cancer research and clinical oncology
Mar 28, 2025
PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to predict treatment response in elderly patients with diffuse large B-cell lymphoma (DLBCL).
Cyclophosphamide
Fluorodeoxyglucose F18
Prednisone
Rituximab
Vincristine
Prognosis
Antineoplastic Combined Chemotherapy Protocols
Retrospective Studies
Treatment Outcome
Aged, 80 and over
Humans
Female
Radiopharmaceuticals
Radiomics
Machine Learning
Aged
Positron-Emission Tomography
Lymphoma, Large B-Cell, Diffuse
Male
Middle Aged
Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.
Computers in biology and medicine
Mar 23, 2025
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
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