Literature DB >> 29959149

LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity.

Christophe Nioche1, Fanny Orlhac1, Sarah Boughdad1, Sylvain Reuzé2, Jessica Goya-Outi1, Charlotte Robert2, Claire Pellot-Barakat1, Michael Soussan1,3, Frédérique Frouin1, Irène Buvat4.   

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. ©2018 American Association for Cancer Research.

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Year:  2018        PMID: 29959149     DOI: 10.1158/0008-5472.CAN-18-0125

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  199 in total

1.  Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach.

Authors:  Yi Zhou; Xue-Lei Ma; Ting Zhang; Jian Wang; Tao Zhang; Rong Tian
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-02-05       Impact factor: 9.236

2.  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

3.  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

4.  Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients.

Authors:  Pierpaolo Alongi; Alessandro Stefano; Albert Comelli; Riccardo Laudicella; Salvatore Scalisi; Giuseppe Arnone; Stefano Barone; Massimiliano Spada; Pierpaolo Purpura; Tommaso Vincenzo Bartolotta; Massimo Midiri; Roberto Lagalla; Giorgio Russo
Journal:  Eur Radiol       Date:  2021-01-14       Impact factor: 5.315

5.  Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging.

Authors:  Hubert Beaumont; Antoine Iannessi; Anne-Sophie Bertrand; Jean Michel Cucchi; Olivier Lucidarme
Journal:  Eur Radiol       Date:  2021-01-18       Impact factor: 5.315

6.  Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features.

Authors:  Yang Zhang; Chaoyue Chen; Zerong Tian; Jianguo Xu
Journal:  Jpn J Radiol       Date:  2020-07-25       Impact factor: 2.374

7.  Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery.

Authors:  Masatoshi Hotta; Ryogo Minamimoto; Yoshimasa Gohda; Kenta Miwa; Kensuke Otani; Tomomichi Kiyomatsu; Hideaki Yano
Journal:  Ann Nucl Med       Date:  2021-05-04       Impact factor: 2.668

8.  Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

Authors:  Burak Kocak; Emine Sebnem Durmaz; Ece Ates; Ipek Sel; Saime Turgut Gunes; Ozlem Korkmaz Kaya; Amalya Zeynalova; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2019-11-05       Impact factor: 5.315

9.  Predictive Role of Temporal Changes in Intratumoral Metabolic Heterogeneity During Palliative Chemotherapy in Patients with Advanced Pancreatic Cancer: A Prospective Cohort Study.

Authors:  Shin Hye Yoo; Seo Young Kang; Gi Jeong Cheon; Do-Youn Oh; Yung-Jue Bang
Journal:  J Nucl Med       Date:  2019-06-14       Impact factor: 10.057

10.  Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient.

Authors:  Manuel Weber; Lukas Kessler; Benedikt Schaarschmidt; Wolfgang Peter Fendler; Harald Lahner; Gerald Antoch; Lale Umutlu; Ken Herrmann; Christoph Rischpler
Journal:  BMC Cancer       Date:  2020-04-16       Impact factor: 4.430

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