Scientific publications - Median Technologies

Elke Anklam [1], Martin Iain Bahl [2], Robert Ball [3] et al. - Affiliations: [1] Joint Research Centre, EU. [2] National Food Institute, Technical University, Denmark. [3] U.S. Food and Drug Administration.

This scientific paper was published in Experiment Biology and Medicine Journal and led by the U.S. Food and Drug Administration. It summarized selected presentations from the Global Summit on Regulatory Science (GSRS20), and highlighted the importance for regulatory community to work closely with the technology developers for emerging global regulatory challenges. Dr. Yan Liu (MD and PhD), Chief Medical Officer at Median Technologies was selected to participate in the elaboration of this paper.

Yan Liu [1], Benoit Huet [1] – Affiliations: [1] Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France.

Presentation held on Nov. 28, 2021 at the RSNA AI Theater – RSNA Annual Meeting, Nov. 28 – Dec. 2, 2021, Chicago, IL, USA. The presentation provides an overview of iBiopsy® Lung Cancer Screening CADx performance with a focus on stage 1 lung cancer characterization.

Hubert Beaumont [1] , Antoine Iannessi [1, 2], Yi Wang [1], Charles M. Voyton [1], Jennifer Cillario [1], Yan Liu [1] – Affiliations: [1] Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France. [2] Centre Antoine Lacassagne, 33 Avenue de Valombrose, 06100 Nice, France

The aim of this study was to analyze a pool of lung trials that used RECIST 1.1, document the proportion of reader discrepancies and the reader performance through monitoring procedures, and provide suggestions for the reduction of read inconsistency.

Hubert Beaumont [1], Antoine Iannessi [1], Jean Michel Cucchi [2], Anne-Sophie Bertand [3] , Olivier Lucidarme [4] – Affiliations: [1] Median Technologies, Valbonne, France. [2] Centre d’Imagerie Medical de Monaco, Monaco. [3] Centre Hospitalier Princess Grâce, Monaco. [4] Hôpital La Pitiè Salepétrière, Paris, France

This paper studies the repeatability and the relative intra-scan variability across acquisition protocols in CT using phantom and unenhanced abdominal series.

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.