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. 2016 May 24:4:e2057.
doi: 10.7717/peerj.2057. eCollection 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

Andriy Fedorov  1 , David Clunie  2 , Ethan Ulrich  3 , Christian Bauer  3 , Andreas Wahle  3 , Bartley Brown  4 , Michael Onken  5 , Jörg Riesmeier  6 , Steve Pieper  7 , Ron Kikinis  8 , John Buatti  9 , Reinhard R Beichel  10
Affiliations

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

Andriy Fedorov et al. PeerJ. .

Abstract

Background. Imaging biomarkers hold tremendous promise for 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 tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support 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. 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 using manual and semi-automatic approaches, automatic segmentation of the reference regions, 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 and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing 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 in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). 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 the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.

Keywords: Cancer imaging; DICOM; Head and neck cancer; Image analysis; Imaging biomarker; Imaging informatics; Interoperability; Open science; PET/CT imaging; Quantitative imaging.

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Conflict of interest statement

David Clunie is the owner of PixelMed Publishing, LLC, Bangor, Pennsylvania, USA; Michael Onken is an employee of Open Connections GmbH; Jörg Riesmeier is a freelancer in Oldenburg, Germany; Steve Pieper is an employee of Isomics, Inc., Cambridge, Massachusetts, USA; and Ron Kikinis is an employee of Fraunhofer MEVIS, Bremen, Germany. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCI/NIH.

Figures

Figure 1
Figure 1. Diagram of the interaction among the various data sources and processing steps that result in the dataset described in this paper.
Components of the dataset represented in DICOM are released publicly within The Cancer Imaging Archive (TCIA) QIN-HEADNECK collection. FOSS tools corresponding to the processing steps other than Reference Region (RR) segmentation (processing steps with the dashed outline) are available.
Figure 2
Figure 2. An illustration of the relationships among the DICOM objects discussed in this manuscript.
DICOM PET/CT is the original dataset obtained by the imaging equipment and is modified only by the de-identification procedure. DICOM RWVM, SEG, and measurement SR are derived objects. DICOM SR with the clinical information encodes the information about the patient originally stored in the relational database. Solid lines denote explicit reference of the object instances by the derived objects (referenced instance is pointed to by the arrow). Dashed bidirectional arrows denote commonality of identifiers (i.e., common composite context, e.g., at the Patient and Study level).
Figure 3
Figure 3. Relationships of the private DICOM SR templates used for encoding of the clinical information.
The top-level Clinical Data Report template incorporates subordinate templates, described in detail in Appendix S2.
Figure 4
Figure 4. The family of DICOM SR templates used for communicating the PET measurements.
All of the templates used to encode derived measurements are included in the DICOM standard.
Figure 5
Figure 5. An attribute-level dump corresponding to the section of the DICOM SR measurements.
The text shown is an excerpt of the complete object dump encoding SUVbw peak value for subject QIN-HEADNECK-01-0024, series “tumor measurements—User1 Manual trial 1”, as displayed in the Atom editor using dicom-dump package.
Figure 6
Figure 6. An XML representation corresponding to the section of the DICOM SR measurements.
The excerpt shown is encoding SUVbw peak measurement for subject QIN-HEADNECK-01-0024, series “tumor measurements—User1 Manual trial 1.”
Figure 7
Figure 7. A rendered view of an HTML representation of the SR measurements object tree.
The content shown is for subject QIN-HEADNECK-01-0024, series “tumor measurements—User1 Manual trial 1,” as generated by the DCMTK dsr2html tool and rendered in a Chrome browser. SUVbw peak measurement is highlighted by the red rectangle.
Figure 8
Figure 8. Example of the segmentation results visualization initialized from DICOM representation.
Shown is subject QIN-HEADNECK-01-00024, as displayed in 3D Slicer software. The primary tumor is shown in green and the lymph node metastasis in yellow. (A): overlay of the secondary tumor outline in yellow over a coronal reformat of the SUV-normalized PET volume. (B): overlay of the secondary tumor outline and SUV-normalized PET volume thresholded to highlight the areas of uptake over a coronal reformat of the CT volume. (C): maximum intensity projection (MIP) view of the PET volume composed with the surface rendering of both the primary and secondary tumors.
See this image and copyright information in PMC

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