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.
Highlights
Texture analysis has been suggested since the early 1980s as a way to extract relevant information characterizing tissue types from various medical images
Various research software programs that enable the calculation of radiomic features (RF) have been developed, easy-to-install and user-friendly software that can be interfaced with the Picture Archiving and Communication System is not yet widely available
In an effort to boost the promising research regarding the potential and use of textural analysis in medical imaging, we developed freeware dedicated to the easy calculation of RF from medical images that does not require any specific programming skills
Summary
Texture analysis has been suggested since the early 1980s as a way to extract relevant information characterizing tissue types from various medical images. Corresponding Author: Irene Buvat, Imagerie Moleculaire In Vivo Lab, UMR 1023 Inserm/CEA/Universite Paris Sud, ERL 9218 CNRS, CEA/Service Hospitalier Frederic Joliot, 4 Place de General Leclerc, Orsay 91400, France.
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