Literature DB >> 27271051

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

Mathieu Hatt1, Florent Tixier2,3, Larry Pierce4, Paul E Kinahan4, Catherine Cheze Le Rest2,3, Dimitris Visvikis5.   

Abstract

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Entities:  

Keywords:  Critical review; Heterogeneity; Image texture; PET/CT; Recommendations

Mesh:

Year:  2016        PMID: 27271051      PMCID: PMC5283691          DOI: 10.1007/s00259-016-3427-0

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  107 in total

1.  Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT.

Authors:  Tadashi Watabe; Mitsuaki Tatsumi; Hiroshi Watabe; Kayako Isohashi; Hiroki Kato; Masahiro Yanagawa; Eku Shimosegawa; Jun Hatazawa
Journal:  Ann Nucl Med       Date:  2011-12-21       Impact factor: 2.668

2.  Staging of cervical cancer based on tumor heterogeneity characterized by texture features on (18)F-FDG PET images.

Authors:  Wei Mu; Zhe Chen; Ying Liang; Wei Shen; Feng Yang; Ruwei Dai; Ning Wu; Jie Tian
Journal:  Phys Med Biol       Date:  2015-06-17       Impact factor: 3.609

3.  18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

Authors:  Mathieu Hatt; Mohamed Majdoub; Martin Vallières; Florent Tixier; Catherine Cheze Le Rest; David Groheux; Elif Hindié; Antoine Martineau; Olivier Pradier; Roland Hustinx; Remy Perdrisot; Remy Guillevin; Issam El Naqa; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2014-12-11       Impact factor: 10.057

4.  Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging. The Visual Response Score and the Change in Total Lesion Glycolysis.

Authors:  Steven M. Larson; Yusuf Erdi; Timothy Akhurst; Madhu Mazumdar; Homer A. Macapinlac; Ronald D. Finn; Cecille Casilla; Melissa Fazzari; Neil Srivastava; Henry W.D. Yeung; John L. Humm; Jose Guillem; Robert Downey; Martin Karpeh; Alfred E. Cohen; Robert Ginsberg
Journal:  Clin Positron Imaging       Date:  1999-05

5.  MR tissue characterization of intracranial tumors by means of texture analysis.

Authors:  L R Schad; S Blüml; I Zuna
Journal:  Magn Reson Imaging       Date:  1993       Impact factor: 2.546

6.  MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection.

Authors:  Andrew Cameron; Farzad Khalvati; Masoom A Haider; Alexander Wong
Journal:  IEEE Trans Biomed Eng       Date:  2015-10-01       Impact factor: 4.538

7.  Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis.

Authors:  Jia Wu; Todd Aguilera; David Shultz; Madhu Gudur; Daniel L Rubin; Billy W Loo; Maximilian Diehn; Ruijiang Li
Journal:  Radiology       Date:  2016-04-05       Impact factor: 11.105

8.  The promise and limits of PET texture analysis.

Authors:  Nai-Ming Cheng; Yu-Hua Dean Fang; Tzu-Chen Yen
Journal:  Ann Nucl Med       Date:  2013-08-13       Impact factor: 2.668

9.  Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer.

Authors:  Jasmine A Oliver; Mikalai Budzevich; Geoffrey G Zhang; Thomas J Dilling; Kujtim Latifi; Eduardo G Moros
Journal:  Transl Oncol       Date:  2015-12       Impact factor: 4.243

10.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

Authors:  Ralph T H Leijenaar; Georgi Nalbantov; Sara Carvalho; Wouter J C van Elmpt; Esther G C Troost; Ronald Boellaard; Hugo J W L Aerts; Robert J Gillies; Philippe Lambin
Journal:  Sci Rep       Date:  2015-08-05       Impact factor: 4.379

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  136 in total

1.  [18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type.

Authors:  Wei-Chih Shen; Shang-Wen Chen; Ji-An Liang; Te-Chun Hsieh; Kuo-Yang Yen; Chia-Hung Kao
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-04-14       Impact factor: 9.236

2.  Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

Authors:  Zisha Zhong; Yusung Kim; Kristin Plichta; Bryan G Allen; Leixin Zhou; John Buatti; Xiaodong Wu
Journal:  Med Phys       Date:  2019-01-04       Impact factor: 4.071

3.  Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.

Authors:  Alex Zwanenburg
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-25       Impact factor: 9.236

4.  AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

Authors:  Isabella Castiglioni; Francesca Gallivanone; Paolo Soda; Michele Avanzo; Joseph Stancanello; Marco Aiello; Matteo Interlenghi; Marco Salvatore
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-11       Impact factor: 9.236

5.  Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography.

Authors:  Wei-Chih Shen; Shang-Wen Chen; Kuo-Chen Wu; Te-Chun Hsieh; Ji-An Liang; Yao-Ching Hung; Lian-Shung Yeh; Wei-Chun Chang; Wu-Chou Lin; Kuo-Yang Yen; Chia-Hung Kao
Journal:  Eur Radiol       Date:  2019-05-27       Impact factor: 5.315

6.  Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.

Authors:  Mathieu Hatt; John A Lee; Charles R Schmidtlein; Issam El Naqa; Curtis Caldwell; Elisabetta De Bernardi; Wei Lu; Shiva Das; Xavier Geets; Vincent Gregoire; Robert Jeraj; Michael P MacManus; Osama R Mawlawi; Ursula Nestle; Andrei B Pugachev; Heiko Schöder; Tony Shepherd; Emiliano Spezi; Dimitris Visvikis; Habib Zaidi; Assen S Kirov
Journal:  Med Phys       Date:  2017-05-18       Impact factor: 4.071

Review 7.  Using PET for therapy monitoring in oncological clinical trials: challenges ahead.

Authors:  C M Deroose; S Stroobants; Y Liu; L K Shankar; P Bourguet
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-04-27       Impact factor: 9.236

8.  Personalized medicine: a new option for nuclear medicine and molecular imaging in the third millennium.

Authors:  Orazio Schillaci; Nicoletta Urbano
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-04       Impact factor: 9.236

Review 9.  Texture analysis of medical images for radiotherapy applications.

Authors:  Elisa Scalco; Giovanna Rizzo
Journal:  Br J Radiol       Date:  2016-11-25       Impact factor: 3.039

10.  Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer.

Authors:  Seung Hwan Moon; Jinho Kim; Je-Gun Joung; Hongui Cha; Woong-Yang Park; Jin Seok Ahn; Myung-Ju Ahn; Keunchil Park; Joon Young Choi; Kyung-Han Lee; Byung-Tae Kim; Se-Hoon Lee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-08-25       Impact factor: 9.236

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