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.
Highlights
Imaging has enormous untapped potential to improve clinical cancer treatment decision making
The Cancer Imaging Archive (TCIA) was selected for archival of the resulting data since it is the Quantitative Imaging Network (QIN)recommended data sharing platform, and the analysis generating the encoded data was done as part of the QIN activities at the University of Iowa
We have presented a detailed investigation of the development and application of the DICOM standard and supporting Free and Open Source Software (FOSS) tools to encode research data for quantitative imaging biomarker development
Summary
Imaging has enormous untapped potential to improve clinical cancer treatment decision making. There is lack of an infrastructure to support common data exchange and method sharing These issues hinder development, validation and comparison of new approaches, secondary analysis and discovery of data, and comparison of results across sites. The overarching mission of QIICR is to provide Free and Open Source Software (FOSS) QI analysis tools accompanied by the imaging data and analysis results stored in a standardscompliant structured fashion to support imaging biomarker development, and facilitate both the reuse of the shared research data and the acceleration of the translation of those usecases into clinical practice. 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. The resulting annotated objects are amenable for data mining applications, and are interoperable with a variety of systems that adopt the DICOM standard
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