Mr. Omar El Gazzar Profile

Omar El Gazzar

at OFFIS eV

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Proceedings Article | 16 February 2012 Paper

A comprehensive framework for quality assurance in clinical trials

Omar El Gazzar, Michael Onken, Marco Eichelberg, Andreas Hein, Elmar Kotter

Proceedings Volume 8319, 83190A (2012) https://doi.org/10.1117/12.910872

KEYWORDS: Clinical trials, Image quality, Medical imaging, Reconstruction algorithms, Software development, Computed tomography, Tumors, Manufacturing, X-ray imaging, X-rays

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Biomarkers captured by medical images are increasingly used as indicators for the efficacy or safety of a certain drug or treatment for clinical trials. For example, medical images such as CT or MR are often used for extracting quantitative measurements for the assessment of tumor treatment response while evaluating a chemotherapy drug for therapeutic cancer trials. Quality assurance is defined as "All those planned and systematic actions that are established to ensure that the trial is performed and the data are generated, documented (recorded), and reported in compliance with good clinical practice (GCP) and the applicable regulatory requirement(s)" [1]. Our objective is to build a generalized and an automated framework for quality assurance within the clinical trials workflow. In order to reach this goal, a set of standardized software tools have been developed for quality assurance. Furthermore, we outline some guidelines as recommendations for the users handling the image data within the research workflow. The software tools developed include tools for image selection, image pseudonymization and image quality conformance check. The export tools are developed based on the specifications of the Integrating the Healthcare Enterprise (IHE) Teaching and Clinical Trial Export (TCE) profile. A DICOM-based quality conformance approach has been developed by validating the DICOM header attributes required for a certain imaging application (e.g. CAD, MPR, 3D) and comparing imaging acquisition parameters against the protocol specification. A formal description language is used to represent such quality requirements. For evaluation, imaging data collected from a clinical trial site were validated against Multi-Planar Reconstruction (MPR). We found that out of 60 studies, about 30% of image series volumes failed the MPR check for some common reasons.

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