White papers

This whitepaper, developed from a survey by Median Technologies and Citeline, delves into the complexities of managing reader variability in radiological imaging for oncology clinical trials. With oncology remaining a major focus for pharmaceutical R&D, ensuring accurate image interpretation is critical for evaluating novel therapies.
The whitepaper highlights the role of Blinded Independent Central Review (BICR) in managing variability and explores strategies for improving image reading accuracy. It also looks ahead to how advanced technologies like AI and machine learning can transform the future of imaging in clinical trials.

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Immunotherapies display novel response patterns that affect the design of imaging based studies and the subsequent evaluation of imaging data. Applying traditional chemotherapy-based response assumptions to immunotherapy trials can result in inaccurate interpretation of response, premature therapy termination, and unnecessary removal of subjects from a trial. Our unique solutions for medical image analysis and management and iBiopsy® for imaging phenotyping, together with our global team of experts, are advancing the development of new drugs and diagnostic tools to monitor disease and assess response to therapy.

Despite decades of research, cancer continues to be the healthcare crisis of this generation. Cancer is now the second leading cause of death globally according to the World Health Organization (WHO) accounting for one in six deaths globally. As global populations age, and chronic diseases become more prevalent in emerging economies, these numbers are expected to soar to nearly 21.7 million new cases and 13 million deaths by 2030. That’s the bad news. The good news is that oncology experts have made tremendous progress in recent years in both the diagnosis and treatment of many forms of cancer. These diagnostic improvements include advanced medical imaging technologies, including digital mammography and computer aided detection (CAD) technologies that use artificial intelligence, and specifically deep learning, to automatically recognize patterns in images that suggest a tumor or lesion.

Medical imaging is an integral component of clinical trials in oncology. For each image type, quantifiable information (referred to as quantitative imaging biomarkers) can be extracted and analyzed to answer questions about tumor types and stage, and to measure response to treatment. The most common assessment of response to therapy is to measure anatomical changes to the tumor. These changes are determined by measuring tumor size in a set of medical images before patient treatment and then measuring this same lesion in a new set of medical images after treatment, thereby measuring how much the tumor has changed with therapy. However, other quantitative imaging measures derived from functional imaging methods provide equally important physiological information about a tumor, including tumor metabolism, vascularity and cellularity. In this white paper, Median Technologies provides a guide to various measurements used in imaging in oncology trials, describing both size-based and functional metrics and the various criteria that use them.