Cargando…

Sensitivity of an AI method for [(18)F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols

BACKGROUND: Convolutional neural networks (CNNs), applied to baseline [(18)F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to differ...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ferrández, Maria C., Golla, Sandeep S. V., Eertink, Jakoba J., de Vries, Bart M., Wiegers, Sanne E., Zwezerijnen, Gerben J. C., Pieplenbosch, Simone, Schilder, Louise, Heymans, Martijn W., Zijlstra, Josée M., Boellaard, Ronald
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Springer Berlin Heidelberg 2023
Materias:	Original Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533444/ https://www.ncbi.nlm.nih.gov/pubmed/37758869 http://dx.doi.org/10.1186/s13550-023-01036-8

Ejemplares similares

Combatting the effect of image reconstruction settings on lymphoma [(18)F]FDG PET metabolic tumor volume assessment using various segmentation methods
por: Ferrández, Maria C., et al.
Publicado: (2022)

An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients
por: Ferrández, Maria C., et al.
Publicado: (2023)

Reproducibility of [18F]FDG PET/CT liver SUV as reference or normalisation factor
por: Zwezerijnen, Gerben J. C., et al.
Publicado: (2022)

External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients
por: Eertink, Jakoba J., et al.
Publicado: (2022)

Prognostic model using (18)F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma
por: Driessen, Julia, et al.
Publicado: (2023)

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features
por: Eertink, Jakoba J., et al.
Publicado: (2022)

(18)F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma
por: Eertink, Jakoba J., et al.
Publicado: (2021)

Blood‐circulating EV‐miRNAs, serum TARC, and quantitative FDG‐PET features in classical Hodgkin lymphoma
por: Drees, Esther E. E., et al.
Publicado: (2022)

Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index
por: Mikhaeel, N. George, et al.
Publicado: (2022)

Baseline radiomics features and MYC rearrangement status predict progression in aggressive B-cell lymphoma
por: Eertink, Jakoba J., et al.
Publicado: (2022)

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma
por: Eertink, J. J., et al.
Publicado: (2023)

3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body (18)F-Fluorodeoxyglucose and (89)Zr-Rituximab PET Scans
por: de Vries, Bart M., et al.
Publicado: (2022)

Predictive value of quantitative (18)F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma
por: Martens, Roland M., et al.
Publicado: (2020)

Optimizing Workflows for Fast and Reliable Metabolic Tumor Volume Measurements in Diffuse Large B Cell Lymphoma
por: Burggraaff, Coreline N., et al.
Publicado: (2020)

Quantitative implications of the updated EARL 2019 PET–CT performance standards
por: Kaalep, Andres, et al.
Publicado: (2019)

Effects of Tracer Uptake Time in Non–Small Cell Lung Cancer (18)F-FDG PET Radiomics
por: Kolinger, Guilherme D., et al.
Publicado: (2022)

Plausibility and redundancy analysis to select FDG‐PET textural features in non‐small cell lung cancer
por: Pfaehler, Elisabeth, et al.
Publicado: (2021)

Aberrant patterns of PET response during treatment for DLBCL patients with MYC gene rearrangements
por: Eertink, J. J., et al.
Publicado: (2021)

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [(18)F]FDG-PET (PETMETPAT)
por: Kogan, Rosalie V., et al.
Publicado: (2019)

Early Response Prediction of Multiparametric Functional MRI and (18)F-FDG-PET in Patients with Head and Neck Squamous Cell Carcinoma Treated with (Chemo)Radiation
por: Martens, Roland M., et al.
Publicado: (2022)

The Additional Value of Ultrafast DCE-MRI to DWI-MRI and 18F-FDG-PET to Detect Occult Primary Head and Neck Squamous Cell Carcinoma
por: Martens, Roland M., et al.
Publicado: (2020)

Multiparametric functional MRI and (18)F-FDG-PET for survival prediction in patients with head and neck squamous cell carcinoma treated with (chemo)radiation
por: Martens, Roland M., et al.
Publicado: (2020)

Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising
por: Golla, Sandeep S. V., et al.
Publicado: (2017)

Updating PET/CT performance standards and PET/CT interpretation criteria should go hand in hand
por: Boellaard, Ronald, et al.
Publicado: (2019)

Variability in quantitative analysis of atherosclerotic plaque inflammation using (18)F-FDG PET/CT
por: Lensen, Karel-Jan D. F., et al.
Publicado: (2017)

Reply to: FDG PET and PET/CT: EANM procedure guideline for tumour PET imaging
por: Boellaard, Ronald, et al.
Publicado: (2010)

Interobserver reproducibility of tumor uptake quantification with (89)Zr-immuno-PET: a multicenter analysis
por: Jauw, Yvonne W. S., et al.
Publicado: (2019)

Model selection criteria for dynamic brain PET studies
por: Golla, Sandeep S. V., et al.
Publicado: (2017)

Characterizing the Bone Marrow Environment in Advanced-Stage Myelofibrosis during Ruxolitinib Treatment Using PET/CT and MRI: A Pilot Study
por: Slot, Stefanie, et al.
Publicado: (2023)

FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0
por: Boellaard, Ronald, et al.
Publicado: (2009)

Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
por: de Vries, Bart M., et al.
Publicado: (2023)

Partial volume correction strategies for quantitative FDG PET in oncology
por: Hoetjes, Nikie J., et al.
Publicado: (2010)

Classification of negative and positive (18)F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network
por: de Vries, Bart Marius, et al.
Publicado: (2020)

FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
por: Boellaard, Ronald, et al.
Publicado: (2014)

Optimized dose regimen for whole-body FDG-PET imaging
por: de Groot, Eleonore H, et al.
Publicado: (2013)

Extracellular vesicle miRNA predict FDG‐PET status in patients with classical Hodgkin Lymphoma
por: Drees, Esther E. E., et al.
Publicado: (2021)

Non-invasive Standardised Uptake Value for Verification of the Use of Previously Validated Reference Region for [(18)F]Flortaucipir and [(18)F]Florbetapir Brain PET Studies
por: de Vries, Bart M., et al.
Publicado: (2021)

Convolutional neural networks for automatic image quality control and EARL compliance of PET images
por: Pfaehler, Elisabeth, et al.
Publicado: (2022)

Mapping heterogeneity in glucose uptake in metastatic melanoma using quantitative (18)F-FDG PET/CT analysis
por: de Heer, Ellen C., et al.
Publicado: (2018)

AI-enhanced simultaneous multiparametric (18)F-FDG PET/MRI for accurate breast cancer diagnosis
por: Romeo, V., et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_b0s80l0udrfi690hp66ch4ck6s