Publications scientifiques

Blinded Independent Central Review (BICR) in New Therapeutic Lung Cancer Trials

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.

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Intra-scan inter-tissue variability can help harmonize radiomics features in CT

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.

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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.

Preliminary Study to Identify the Severity of Hepatic Fibrosis in Patients with Non-Alcoholic Steatohepatitis (NASH) Using iBiopsy®

Jean-Christophe Brisset [1], Benoit Huet [1], Nozha Boujemaa [1] – Affiliations: [1] Median Technologies, Valbonne, France.

The objective of this study was to quantify the ability of iBiopsy®’s algorithms to discriminate between early and advanced fibrosis grade in NASH patients using clinically available tests and images.

RECIST 1.1 and lesion selection: How to deal with ambiguity at baseline?

Antoine Iannessi [1], Hubert Beaumont [1], Yan Liu [1], Anne-Sophie Bertrand [2] – Affiliations: [1] Median Technologies, Valbonne, France. [2] Centre Hospitalier Princesse Grâce, Monaco

The goal of this paper is to provide insights for radiologists faced with equivocal baseline abnormalities and to raise awareness of the potential risks arising from such situations regarding the outcome of clinical trials using the RECIST evaluation technique.

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