Resource library

Videos

Median Technologies: Imaging in Clinical Trials

At Median we’re determined to push the boundaries of what Imaging Technology can achieve, to help develop next-generation treatments with the potential to save or improve the lives of millions. We’re already transforming the use of medical images in Clinical Trials and patient care; extracting the highest quality, most advanced Imaging Biomarkers to set the standard for developing new therapies, diagnosing disease and treating patients. We’re helping biopharmaceutical organizations around the world make quicker, better-informed decisions, bringing new therapies closer every day.

20 May 2021

Median Technologies: Imaging in Clinical Trials

At Median we’re determined to push the boundaries of what Imaging Technology can achieve, to help develop next-generation treatments with the potential to…

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Brochures

Tumor Assessment Using RECIST 1.1 Criteria and Alternative Criteria for Immuno-oncology Trials

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White papers

Understanding Novel Response Patterns of Immunotherapies

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.

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Case studies

FDA Inspection

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Scientific publications

Fully-Learned Features for Lung Cancer Overall Survival Prediction

Danny Francis [1], Vladimir Groza [1], Benoit Huet [1], Nozha Boujemaa [1] – Affiliations: [1] Median Technologies, Valbonne, France.

This work presents an end-to-end trained method and its preliminary results demonstrating the possibility to predict Overall Survival (OS) time for patients with lung cancer.

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