Abstract
.Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials.
Highlights
The field of quantitative imaging is rapidly growing, especially in the area of radiomics and machine learning
All the files produced by the workflow are available for download through a link provided at the bottom of the results, including the log file, the resultant feature file in a comma-separated values (CSV) format, and the configuration file used for that run
Workflow descriptions All workflows that include the Stanford feature extraction code (QIFE) All workflows that include the PyRadiomics feature extraction code All workflows that include feature extraction code other than Quantitative Image Feature Extraction (QIFE) and PyRadiomics All workflows that include feature extraction code for 2-D images All workflows that include the least absolute shrinkage and selection operator (LASSO) prediction tools All workflows that include an image and/or segmentation conversion tool All workflows that contain a segmentation tool All workflows that do not fall into one of the above categories All workflows available on the quantitative image feature pipeline (QIFP)
Summary
The field of quantitative imaging is rapidly growing, especially in the area of radiomics and machine learning. Users are able to upload their own algorithms in Docker containers, which allows the system to evolve and to support code that has been written in a variety of languages This pipeline gives researchers the tools and infrastructure needed to assess and compare the value of combinations of quantitative image features. The QIFP system allows users to complete these tasks in a single pipeline It can allow for the widespread development, assessment, and dissemination of new imaging biomarkers, including the opportunity for external validation of existing software pipelines. This system can be used to facilitate incorporating quantitative imaging tools into single and multicenter clinical and virtual clinical trials involving image processing, radiomics, and/or machine learning. The QIFP could be used as a central webserver where multiple institutions could upload de-identified imaging data and perform standardized image-processing pipelines
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