Cargando…

False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review

PURPOSE: A number of recent publications have proposed that a family of image-derived indices, called texture features, can predict clinical outcome in patients with cancer. However, the investigation of multiple indices on a single data set can lead to significant inflation of type-I errors. We rep...

Descripción completa

Detalles Bibliográficos
Autores principales:	Chalkidou, Anastasia, O’Doherty, Michael J., Marsden, Paul K.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2015
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4418696/ https://www.ncbi.nlm.nih.gov/pubmed/25938522 http://dx.doi.org/10.1371/journal.pone.0124165

Ejemplares similares

Comparing approaches to correct for respiratory motion in NH(3) PET-CT cardiac perfusion imaging
por: Schleyer, Paul J., et al.
Publicado: (2013)

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

Causes and imaging features of false positives and false negatives on (18)F-PET/CT in oncologic imaging
por: Long, Niamh M., et al.
Publicado: (2011)

A method for accurate spatial registration of PET images and histopathology slices
por: Puri, Tanuj, et al.
Publicado: (2015)

False discovery rates in spectral identification
por: Jeong, Kyowon, et al.
Publicado: (2012)

Controlling the Rate of GWAS False Discoveries
por: Brzyski, Damian, et al.
Publicado: (2017)

Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics
por: Prenosil, George Amadeus, et al.
Publicado: (2020)

Diagnostic value of baseline (18)FDG PET/CT skeletal textural features in follicular lymphoma
por: Faudemer, Julie, et al.
Publicado: (2021)

False discovery rates: a new deal
por: Stephens, Matthew
Publicado: (2017)

4497 Accessible False Discovery Rate Computation
por: Hollister, Megan C, et al.
Publicado: (2020)

The functional false discovery rate with applications to genomics
por: Chen, Xiongzhi, et al.
Publicado: (2019)

Feasibility of simultaneous PET-MR perfusion using a novel cardiac perfusion phantom
por: O’Doherty, Jim, et al.
Publicado: (2017)

False-positive FDG PET CT Scan in Vertebral Hemangioma
por: Solav, Shrikant Vasantrao, et al.
Publicado: (2019)

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT
por: Markel, Daniel, et al.
Publicado: (2013)

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
por: Chong, Elizabeth Y., et al.
Publicado: (2015)

Dynamic PET Imaging Using Dual Texture Features
por: Ouyang, Zhanglei, et al.
Publicado: (2022)

Estimation of input functions from dynamic [(18)F]FLT PET studies of the head and neck with correction for partial volume effects
por: Hackett, Sara L, et al.
Publicado: (2013)

Adenocarcinoma with BAC Features Presented as the Nonsolid Nodule Is Prone to Be False-Negative on (18)F-FDG PET/CT
por: Wu, Hu-bing, et al.
Publicado: (2015)

A unified approach to false discovery rate estimation
por: Strimmer, Korbinian
Publicado: (2008)

Testing Jumps via False Discovery Rate Control
por: Yen, Yu-Min
Publicado: (2013)

False discovery rate control in two-stage designs
por: Zehetmayer, Sonja, et al.
Publicado: (2012)

An investigation of the false discovery rate and the misinterpretation of p-values
por: Colquhoun, David
Publicado: (2014)

Importance of presenting the variability of the false discovery rate control
por: Lin, Yi-Ting, et al.
Publicado: (2015)

False discovery rate estimation and heterobifunctional cross-linkers
por: Fischer, Lutz, et al.
Publicado: (2018)

Comparison of false-discovery rates of various decoy databases
por: Lee, Sangjeong, et al.
Publicado: (2021)

FDRestimation: Flexible False Discovery Rate Computation in R
por: Murray, Megan H., et al.
Publicado: (2021)

False discovery rate control in cancer biomarker selection
por: Li, Zhaoming
Publicado: (2023)

Respiration-Averaged CT for Attenuation Correction of PET Images – Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients
por: Cheng, Nai-Ming, et al.
Publicado: (2016)

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers
por: Ahn, Hyein, et al.
Publicado: (2022)

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal (18)F-FDG PET/CT
por: Peng, Lihong, et al.
Publicado: (2023)

Varlociraptor: enhancing sensitivity and controlling false discovery rate in somatic indel discovery
por: Köster, Johannes, et al.
Publicado: (2020)

Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control
por: Zhang, Qingyang, et al.
Publicado: (2019)

Reliability of CT‐based texture features: Phantom study
por: Varghese, Bino A., et al.
Publicado: (2019)

Power, false discovery rate and Winner’s Curse in eQTL studies
por: Huang, Qin Qin, et al.
Publicado: (2018)

On correcting the overestimation of the permutation-based false discovery rate estimator
por: Jiao, Shuo, et al.
Publicado: (2008)

Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics
por: Keich, Uri, et al.
Publicado: (2015)

A note on the false discovery rate of novel peptides in proteogenomics
por: Zhang, Kun, et al.
Publicado: (2015)

A Fuzzy Permutation Method for False Discovery Rate Control
por: Yang, Ya-Hui, et al.
Publicado: (2016)

Weighted false discovery rate controlling procedures for clinical trials
por: Benjamini, Yoav, et al.
Publicado: (2017)

A new estimation of protein-level false discovery rate
por: Wu, Guanying, et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_9rumv0nkervhaunv3r61hh8gmq