DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research
- Subject Areas
- Bioinformatics, Clinical Trials, Oncology, Radiology and Medical Imaging
- quantitative imaging, imaging biomarker, imaging informatics, DICOM, PET/CT imaging, head and neck cancer, image analysis, cancer imaging, interoperability, open science
- © 2016 Fedorov et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ PrePrints 4:e1541v3 https://doi.org/10.7287/peerj.preprints.1541v3
Background. Imaging biomarkers hold tremendous promise in the precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation motivate integration of the clinical and imaging data, and the use of standardized approaches to sharing analysis results and semantics. We develop the methodology and supporting tools to perform these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open source software tools.
Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor and reference regions of interest using manual and semi-automatic approaches, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data.
Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results. A number of correction proposals to the standard were developed. The open source DICOM toolkit (DCMTK) was improved to simplify the task of encoding via new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited to the QIN-HEADNECK collection of The Cancer Imaging Archive. Supporting tools for data analysis and DICOM conversion were made available as free open source software.
Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that DICOM standard can be used to represent various types of the analysis results and encode their complex relationships. The resulting annotated objects are amenable for data mining applications, and are interoperable with a variety of systems that adopt the DICOM standard.
This version includes minor changes to the text to reduce ambiguity and address some of the comments received from the readers. Figure 8 was replaced to improve presentation. This is also the version being submitted for peer review to PeerJ.