Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research

Aditya P Apte; Aditi Iyer; Mireia Crispin-Ortuzar; Rutu Pandya; Lisanne V van Dijk; Emiliano Spezi; Maria Thor; Hyemin Um; Harini Veeraraghavan; Jung Hun Oh; Amita Shukla-Dave; Joseph O Deasy

doi:10.1002/mp.13046

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research

Med Phys. 2018 Jun 13:10.1002/mp.13046. doi: 10.1002/mp.13046. Online ahead of print.

Authors

Affiliations

¹ Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
² Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
³ The University Medical Center, Groningen, The Netherlands.
⁴ Cardiff University, Cardiff, UK.

Abstract

Purpose: Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research.

Method: The radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB^® application programming interface.

Results: The CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested.

Conclusion: The CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses.

Keywords: imaging biomarker; inter-software test; machine learning; open source software; radiomics; reproducibility.

Abstract

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