ibex: An open infrastructure software platform to facilitate collaborative work in radiomics

Abstract

Purpose: Radiomics, which is the high-throughput extraction and analysis of quantitative image features, has been shown to have considerable potential to quantify the tumor phenotype. However, at present, a lack of software infrastructure has impeded the development of radiomics and its applications. Therefore, the authors developed the imaging biomarker explorer (), an open infrastructure software platform that flexibly supports common radiomics workflow tasks such as multimodality image data import and review, development of feature extraction algorithms, model validation, and consistent data sharing among multiple institutions. Methods: The  software package was developed using the  and /++ programming languages. The software architecture deploys the modern model-view-controller, unit testing, and function handle programming concepts to isolate each quantitative imaging analysis task, to validate if their relevant data and algorithms are fit for use, and to plug in new modules. On one hand,  is self-contained and ready to use: it has implemented common data importers, common image filters, and common feature extraction algorithms. On the other hand,  provides an integrated development environment on top of  and /++, so users are not limited to its built-in functions. In the  developer studio, users can plug in, debug, and test new algorithms, extending 's functionality.  also supports quality assurance for data and feature algorithms: image data, regions of interest, and feature algorithm-related data can be reviewed, validated, and/or modified. More importantly, two key elements in collaborative workflows, the consistency of data sharing and the reproducibility of calculation result, are embedded in the  workflow: image data, feature algorithms, and model validation including newly developed ones from different users can be easily and consistently shared so that results can be more easily reproduced between institutions. Results: Researchers with a variety of technical skill levels, including radiation oncologists, physicists, and computer scientists, have found the  software to be intuitive, powerful, and easy to use.  can be run at any computer with the windows operating system and 1GB RAM. The authors fully validated the implementation of all importers, preprocessing algorithms, and feature extraction algorithms. Windows version 1.0 beta of stand-alone  and 's source code can be downloaded. Conclusions: The authors successfully implemented , an open infrastructure software platform that streamlines common radiomics workflow tasks. Its transparency, flexibility, and portability can greatly accelerate the pace of radiomics research and pave the way toward successful clinical translation. C 2015 American Association of Physicists in Medicine. [http://dx

Preliminary investigation into sources of uncertainty in quantitative imaging features

“…Texture features were measured with our texture analysis program, Imaging Biomarker Explorer Software V1.0β (IBEX) [14]. This program is an open source platform for the calculation of texture features from various modalities that provides the user with substantial flexibility in their choice of texture parameters and allows for the isolated testing of each individual feature.…”

The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer

“…The interpreter was masked to other clinical information and to the reference standard. Contours were extracted, and quantitative 18 F-FDG PET analysis was performed using the Imaging Biomarker Explorer software package (27) built in-house with commercial software (Matlab, version 8.4; The MathWorks Inc.).…”

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

“…[6][7][8]10,11,[13][14][15]17,31,32 The features were all calculated using the open-source Imaging Biomarker EXplorer () software, which is available for download at http://bit.ly/ IBEX_MDAnderson. 33 Selected features included first-order descriptors from the intensity histogram (Hist); secondorder features to describe spatial relationships in gray level intensities from the co-occurrence matrix (COM), 17,34 runlength matrix (RLM), 35 and neighborhood gray-tone difference matrix (NGTDM); 36 and Laplacian of Gaussian (LoG) filtered features, which can highlight tumor characteristics not visible in the original image. 6,31 In subsequent tables and figures, features are named for the feature category and then the feature name (e.g., contrast from the COM is listed as COMcontrast).…”