Literature DB >> 31420497

Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts.

Elisabeth Pfaehler1, Joyce van Sluis2, Bram B J Merema3, Peter van Ooijen4, Ralph C M Berendsen5, Floris H P van Velden6, Ronald Boellaard2,7.   

Abstract

The sensitivity of radiomic features to several confounding factors, such as reconstruction settings, makes clinical use challenging. To investigate the impact of harmonized image reconstructions on feature consistency, a multicenter phantom study was performed using 3-dimensionally printed phantom inserts reflecting realistic tumor shapes and heterogeneity uptakes.
Methods: Tumors extracted from real PET/CT scans of patients with non-small cell lung cancer served as model for three 3-dimensionally printed inserts. Different heterogeneity pattern were realized by printing separate compartments that could be filled with different activity solutions. The inserts were placed in the National Electrical Manufacturers Association image-quality phantom and scanned various times. First, a list-mode scan was acquired and 5 statistically equal replicates were reconstructed. Second, the phantom was scanned 4 times on the same scanner. Third, the phantom was scanned on 6 PET/CT systems. All images were reconstructed using EANM Research Ltd. (EARL)-compliant and locally clinically preferred reconstructions. EARL-compliant reconstructions were performed without (EARL1) or with (EARL2) point-spread function. Images were analyzed with and without resampling to 2-mm cubic voxels. Images were discretized with a fixed bin width (FBW) of 0.25 and a fixed bin number (FBN) of 64. The intraclass correlation coefficient (ICC) of each scan setup was calculated and compared across reconstruction settings. An ICC above 0.75 was regarded as high.
Results: The percentage of features yielding a high ICC was largest for the statistically equal replicates (70%-91% for FBN; 90%-96% for FBW discretization). For scans acquired on the same system, the percentage decreased, but most features still resulted in a high ICC (FBN, 52%-63%; FBW, 75%-85%). The percentage of features yielding a high ICC decreased more in the multicenter setting. In this case, the percentage of features yielding a high ICC was larger for images reconstructed with EARL-compliant reconstructions: for example, 40% for EARL1 and 60% for EARL2 versus 21% for the clinically preferred setting for FBW discretization. When discretized with FBW and resampled to isotropic voxels, this benefit was more pronounced.
Conclusion: EARL-compliant reconstructions harmonize a wide range of radiomic features. FBW discretization and a sampling to isotropic voxels enhances the benefits of EARL-compliant reconstructions.
© 2020 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  18F-FDG PET/CT radiomic features; feature harmonization; image reconstruction

Mesh:

Year:  2019        PMID: 31420497      PMCID: PMC7067530          DOI: 10.2967/jnumed.119.229724

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   11.082


  29 in total

1.  Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

Authors:  Matthew J Nyflot; Fei Yang; Darrin Byrd; Stephen R Bowen; George A Sandison; Paul E Kinahan
Journal:  J Med Imaging (Bellingham)       Date:  2015-08-05

Review 2.  Characterization of PET/CT images using texture analysis: the past, the present… any future?

Authors:  Mathieu Hatt; Florent Tixier; Larry Pierce; Paul E Kinahan; Catherine Cheze Le Rest; Dimitris Visvikis
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-06-06       Impact factor: 9.236

3.  Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.

Authors:  Ralph T H Leijenaar; Sara Carvalho; Emmanuel Rios Velazquez; Wouter J C van Elmpt; Chintan Parmar; Otto S Hoekstra; Corneline J Hoekstra; Ronald Boellaard; André L A J Dekker; Robert J Gillies; Hugo J W L Aerts; Philippe Lambin
Journal:  Acta Oncol       Date:  2013-09-09       Impact factor: 4.089

Review 4.  PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology.

Authors:  M Sollini; L Cozzi; L Antunovic; A Chiti; M Kirienko
Journal:  Sci Rep       Date:  2017-03-23       Impact factor: 4.379

5.  Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation.

Authors:  Floris H P van Velden; Gerbrand M Kramer; Virginie Frings; Ida A Nissen; Emma R Mulder; Adrianus J de Langen; Otto S Hoekstra; Egbert F Smit; Ronald Boellaard
Journal:  Mol Imaging Biol       Date:  2016-10       Impact factor: 3.488

6.  Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.

Authors:  Yucheng Zhang; Anastasia Oikonomou; Alexander Wong; Masoom A Haider; Farzad Khalvati
Journal:  Sci Rep       Date:  2017-04-18       Impact factor: 4.379

7.  Repeatability of [18F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients.

Authors:  Guilherme D Kolinger; David Vállez García; Gerbrand M Kramer; Virginie Frings; Egbert F Smit; Adrianus J de Langen; Rudi A J O Dierckx; Otto S Hoekstra; Ronald Boellaard
Journal:  EJNMMI Res       Date:  2019-02-07       Impact factor: 3.138

8.  18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer.

Authors:  Fanny Orlhac; Michaël Soussan; Kader Chouahnia; Emmanuel Martinod; Irène Buvat
Journal:  PLoS One       Date:  2015-12-15       Impact factor: 3.240

9.  Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

Authors:  Baderaldeen A Altazi; Geoffrey G Zhang; Daniel C Fernandez; Michael E Montejo; Dylan Hunt; Joan Werner; Matthew C Biagioli; Eduardo G Moros
Journal:  J Appl Clin Med Phys       Date:  2017-09-11       Impact factor: 2.102

10.  Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics.

Authors:  Nicolas Aide; Thibault Salomon; Cécile Blanc-Fournier; Jean-Michel Grellard; Christelle Levy; Charline Lasnon
Journal:  EJNMMI Res       Date:  2018-12-29       Impact factor: 3.138

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  21 in total

1.  A Guide to ComBat Harmonization of Imaging Biomarkers in Multicenter Studies.

Authors:  Fanny Orlhac; Jakoba J Eertink; Anne-Ségolène Cottereau; Josée M Zijlstra; Catherine Thieblemont; Michel Meignan; Ronald Boellaard; Irène Buvat
Journal:  J Nucl Med       Date:  2021-09-16       Impact factor: 10.057

2.  Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features.

Authors:  Jakoba J Eertink; Gerben J C Zwezerijnen; Matthijs C F Cysouw; Sanne E Wiegers; Elisabeth A G Pfaehler; Pieternella J Lugtenburg; Bronno van der Holt; Otto S Hoekstra; Henrica C W de Vet; Josée M Zijlstra; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-08-04       Impact factor: 10.057

3.  Effects of Tracer Uptake Time in Non-Small Cell Lung Cancer 18F-FDG PET Radiomics.

Authors:  Guilherme D Kolinger; David Vállez García; Gerbrand Maria Kramer; Virginie Frings; Gerben J C Zwezerijnen; Egbert F Smit; Adrianus Johannes de Langen; Irène Buvat; Ronald Boellaard
Journal:  J Nucl Med       Date:  2021-12-21       Impact factor: 11.082

4.  Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer.

Authors:  Chunyan Duan; W Art Chaovalitwongse; Fangyun Bai; Daniel S Hippe; Shouyi Wang; Phawis Thammasorn; Larry A Pierce; Xiao Liu; Jianxin You; Robert S Miyaoka; Hubert J Vesselle; Paul E Kinahan; Ramesh Rengan; Jing Zeng; Stephen R Bowen
Journal:  Phys Med Biol       Date:  2020-10-07       Impact factor: 3.609

Review 5.  Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs.

Authors:  Alejandra Valladares; Thomas Beyer; Ivo Rausch
Journal:  Med Phys       Date:  2020-02-12       Impact factor: 4.071

6.  A FDG-PET radiomics signature detects esophageal squamous cell carcinoma patients who do not benefit from chemoradiation.

Authors:  Yimin Li; Marcus Beck; Tom Päßler; Chen Lili; Wu Hua; Ha Dong Mai; Holger Amthauer; Matthias Biebl; Peter C Thuss-Patience; Jasmin Berger; Carmen Stromberger; Ingeborg Tinhofer; Jochen Kruppa; Volker Budach; Frank Hofheinz; Qin Lin; Sebastian Zschaeck
Journal:  Sci Rep       Date:  2020-10-19       Impact factor: 4.379

7.  Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor.

Authors:  Virginia Liberini; Bruno De Santi; Osvaldo Rampado; Elena Gallio; Beatrice Dionisi; Francesco Ceci; Giulia Polverari; Philippe Thuillier; Filippo Molinari; Désirée Deandreis
Journal:  EJNMMI Phys       Date:  2021-02-27

8.  Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer.

Authors:  Elisabeth Pfaehler; Liesbet Mesotten; Ivan Zhovannik; Simone Pieplenbosch; Michiel Thomeer; Karolien Vanhove; Peter Adriaensens; Ronald Boellaard
Journal:  Med Phys       Date:  2021-02-06       Impact factor: 4.071

9.  Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.

Authors:  Matthijs C F Cysouw; Bernard H E Jansen; Tim van de Brug; Daniela E Oprea-Lager; Elisabeth Pfaehler; Bart M de Vries; Reindert J A van Moorselaar; Otto S Hoekstra; André N Vis; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-07-31       Impact factor: 9.236

10.  Quantitative Radiomics Features in Diffuse Large B-Cell Lymphoma: Does Segmentation Method Matter?

Authors:  Jakoba J Eertink; Elisabeth A G Pfaehler; Sanne E Wiegers; Tim van; Pieternella J Lugtenburg; Otto S Hoekstra; Josée M Zijlstra; Henrica C W de Vet; Ronald Boellaard
Journal:  J Nucl Med       Date:  2021-07-16       Impact factor: 10.057

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