Mot-clé
  • 07/22/2026

    Technical performance of L3 skeletal muscle area (SMA) measurement on CT for L3 skeletal muscle index (L3SMI) assessment

    This study provides a comprehensive evaluation of the technical performance of CT-based L3 skeletal muscle index (L3SMI) assessment, an imaging-derived body composition biomarker increasingly used to evaluate sarcopenia and body composition in oncology. By examining accuracy, reproducibility, linearity, and repeatability across multiple cancer populations, the work demonstrates the robustness of automated skeletal muscle measurements while identifying technical limitations and exclusion criteria that can impact reliable assessment.

    These findings offer practical guidance for the integration of L3SMI into clinical research and oncology development programs. By establishing performance benchmarks and highlighting conditions that may affect measurement quality, the study helps sponsors, CROs, and imaging teams improve standardization, strengthen biomarker reliability, and increase confidence in the use of body composition endpoints for patient monitoring, stratification, and longitudinal treatment evaluation.


    H. Beaumont [1], E. Khayat [1], A. Thines [1], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France,
    En savoir plus Téléchargement Technical-performance-of-L3-skeletal-muscle-area-SMA-measurement-on-CT-for-L3-skeletal-muscle-index-L3SMI-assessment.pdf
  • 02/23/2026

    What are RECIST 1.1 progressions made of? Variability in double-read oncology trials

    The study offers an in‑depth look at how RECIST 1.1 progression events are defined and where variability between independent readers can emerge. By shedding light on the mechanisms that influence PD assessments, this work provides valuable insights for strengthening the reliability and consistency of imaging endpoints in oncology trials.

    These insights bring tangible value to both sponsors and CROs, helping teams better anticipate imaging‑related risks, design stronger study frameworks, and reinforce the consistency of interpretation across global programs. Strengthening these foundations ultimately supports more confident decision‑making throughout oncology development.


    H. Beaumont [1], L. Cantini[2], K. Saini [2], N. Faye [1], R. Gill [3], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France, [2]Fortrea Inc., [3] Durham, NC, USA Columbia University Vagelos College of Physicians and Surgeons University Medical Center, New York, NY, USA
    En savoir plus Téléchargement what_are_PD_ER_2026.pdf
  • 02/23/2026

    Rethinking lung cancer screening: longitudinal AI/ML diagnostics beyond nodule size growth

    Présentation scientifique à RSNA 2025


    S. Bodard [1, 2], P. Baudot [3], B. Renoust [3], C.Voyton [3], G. De Bie [3], E. Geremia [3], V. Le [3], D. Francis [3], P. Siot [3], Y. Haddou [3], V. Bourdès [3], B. Huet [3] – Affiliations: [1] Paris Cité University, AP-HP, Necker University Hospital, Adult Imaging Department, Paris, France., [2] Sorbonne University, CNRS, INSERM, Laboratory of Biomedical Imaging, Paris, France.,[3] Median Technologies, Valbonne, France.
    Téléchargement RSNA2025BodardRethinkingLCS.pdf
  • 02/23/2026

    Radiologists’ perception on AI/ML software as a medical device (SaMD) unveiled via post-study usability survey: key assets to redefine lung cancer screening

    Poster présenté à ESMO AI 2025


    F Grossi [1], L. Seijo [2], R. Osarogiagbon [3], C. Gotera [4], E. Ostrin [5], A. Vachani [6], S. Haddag [7], S. Baraghini [7], L. Boy Machefer [7], C. Voyton [7], V. Bourdes [7] – Affiliations:[1] Medical Oncology Division, IRCCS Policlinico San Martino, Genova, Italy, [2] Pulmonology, Clinica Universidad de Navarra, Madrid, Spain, [3] Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, TN, US, [4] Pulmonology, Hospital Universitario Fundacion Jimenez Diaz, Madrid, Spain, [5] Pulmonary Medecine, MD Anderson Cancer Center, Houston, TX, US, [6] Pulmonary and Critical Care, NTU Langone Health NY, US, [7] Median Technologies, Valbonne, France.
    Téléchargement ESMO-AI-2025-Poster_Median_eyonis_ESMO2025-PDF-160X90.pdf
  • 05/24/2025

    Technical performance of the L3 Skeletal Muscle Index in CT

    Our comprehensive evaluation of L3-SMI’s bias, repeatability, reproducibility, and linearity establishes the basis for associating confidence intervals with its measurements. This enables the detection of significant patient changes, laying a strong foundation for L3-SMI’s clinical qualification as a reliable biomarker in health assessments.

    Abstract #e24073 publié par ASCO 2025


    H. Beaumont [1], E. Khayat, A. Thinnes [1], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France.
    En savoir plus Téléchargement ASCO2025-e-abstract-Technical-performance-of-the-L3-Skeletal-Muscle-Index-in-CT.pdf
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