LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity
Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of convent...
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| Published in | Cancer research (Chicago, Ill.) Vol. 78; no. 16; pp. 4786 - 4789 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Association for Cancer Research
15.08.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.
Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR. |
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| AbstractList | Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.
This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities.
. Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy. Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR. Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786-9. ©2018 AACR.Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786-9. ©2018 AACR. |
| Author | Frouin, Frédérique Buvat, Irène Reuzé, Sylvain Goya-Outi, Jessica Boughdad, Sarah Orlhac, Fanny Nioche, Christophe Robert, Charlotte Pellot-Barakat, Claire Soussan, Michael |
| Author_xml | – sequence: 1 givenname: Christophe surname: Nioche fullname: Nioche, Christophe – sequence: 2 givenname: Fanny surname: Orlhac fullname: Orlhac, Fanny – sequence: 3 givenname: Sarah surname: Boughdad fullname: Boughdad, Sarah – sequence: 4 givenname: Sylvain surname: Reuzé fullname: Reuzé, Sylvain – sequence: 5 givenname: Jessica surname: Goya-Outi fullname: Goya-Outi, Jessica – sequence: 6 givenname: Charlotte surname: Robert fullname: Robert, Charlotte – sequence: 7 givenname: Claire surname: Pellot-Barakat fullname: Pellot-Barakat, Claire – sequence: 8 givenname: Michael surname: Soussan fullname: Soussan, Michael – sequence: 9 givenname: Frédérique orcidid: 0000-0002-7288-1543 surname: Frouin fullname: Frouin, Frédérique – sequence: 10 givenname: Irène surname: Buvat fullname: Buvat, Irène |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29959149$$D View this record in MEDLINE/PubMed https://hal.science/hal-01938545$$DView record in HAL |
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| ContentType | Journal Article |
| Copyright | 2018 American Association for Cancer Research. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: 2018 American Association for Cancer Research. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | AAYXX CITATION NPM 7X8 1XC |
| DOI | 10.1158/0008-5472.CAN-18-0125 |
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| References | Orlhac (2022061706194422500_bib9) Fomel (2022061706194422500_bib10) 2009; 11 Zhao (2022061706194422500_bib7) 2016; 6 Orlhac (2022061706194422500_bib13) 2016; 57 Alic (2022061706194422500_bib1) 2014; 9 Yan (2022061706194422500_bib6) 2015; 56 Fisher (2022061706194422500_bib2) 2013; 108 Orlhac (2022061706194422500_bib12) 2015; 10 Nyflot (2022061706194422500_bib5) 2015; 2 Gillies (2022061706194422500_bib3) 2016; 278 Reuzé (2022061706194422500_bib8) 2017; 8 Leijenaar (2022061706194422500_bib11) 2015; 5 Chalkidou (2022061706194422500_bib4) 2015; 10 |
| References_xml | – volume: 5 start-page: 11075 year: 2015 ident: 2022061706194422500_bib11 article-title: The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis publication-title: Sci Rep doi: 10.1038/srep11075 – volume: 57 start-page: 1823 year: 2016 ident: 2022061706194422500_bib13 article-title: Multi-scale texture analysis: from 18F-FDG PET images to pathological slides publication-title: J Nucl Med doi: 10.2967/jnumed.116.173708 – ident: 2022061706194422500_bib9 article-title: A post-reconstruction harmonization method for multicenter radiomic studies in PET publication-title: J Nucl Med. – volume: 10 start-page: e0145063 year: 2015 ident: 2022061706194422500_bib12 article-title: 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer publication-title: PLoS One doi: 10.1371/journal.pone.0145063 – volume: 108 start-page: 479 year: 2013 ident: 2022061706194422500_bib2 article-title: Cancer heterogeneity: implications for targeted therapeutics publication-title: Br J Cancer doi: 10.1038/bjc.2012.581 – volume: 8 start-page: 43169 year: 2017 ident: 2022061706194422500_bib8 article-title: Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners publication-title: Oncotarget doi: 10.18632/oncotarget.17856 – volume: 56 start-page: 1667 year: 2015 ident: 2022061706194422500_bib6 article-title: Impact of image reconstruction settings on texture features in 18F-FDG PET publication-title: J Nucl Med doi: 10.2967/jnumed.115.156927 – volume: 10 start-page: e0124165 year: 2015 ident: 2022061706194422500_bib4 article-title: False discovery rates in PET and CT studies with texture features: a systematic review publication-title: PLoS One doi: 10.1371/journal.pone.0124165 – volume: 11 start-page: 5 year: 2009 ident: 2022061706194422500_bib10 article-title: Introduction: reproducible research publication-title: Comput Sci Eng doi: 10.1109/MCSE.2009.14 – volume: 278 start-page: 563 year: 2016 ident: 2022061706194422500_bib3 article-title: Radiomics: images are more than pictures, they are data publication-title: Radiology doi: 10.1148/radiol.2015151169 – volume: 6 start-page: 23428 year: 2016 ident: 2022061706194422500_bib7 article-title: Reproducibility of radiomics for deciphering tumor phenotype with imaging publication-title: Sci Rep doi: 10.1038/srep23428 – volume: 9 start-page: e110300 year: 2014 ident: 2022061706194422500_bib1 article-title: Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review publication-title: PLoS One doi: 10.1371/journal.pone.0110300 – volume: 2 start-page: 041002 year: 2015 ident: 2022061706194422500_bib5 article-title: Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards publication-title: J Med Imaging doi: 10.1117/1.JMI.2.4.041002 |
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