Mot-clé
  • 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
  • 05/24/2025

    Using tumor growth modeling and informed neural networks as early predictive clinical endpoints

    This study evaluates the utility of TGM within formed neural networks in predicting response and durability. Our findings suggest that early tumor growth parameters, may serve as predictive clinical endpoints for response and long-term outcomes.

    Abstract #e13590 publié par ASCO 2025


    M. Felfli [1], S. Jacques [1], A. Thinnes [1], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France.
    En savoir plus Téléchargement ASCO2025-e-abstract_Using-tumor-growth-modeling-and-informed-neural-networks-as-early-predictive-clinical-endpoints.pdf
  • 05/16/2025

    AI-assisted Lung Cancer Screening: Results from REALITY, a pivotal validation study of an AI/ML-based software

    The AI/ML-based software demonstrates a high level of performance in a multicenter validation cohort enriched for cancer prevalence, cancer stage, and small non-spiculated cancer nodules, with high sensitivity across nodule size and cancer stage. This AI/ML-based software demonstrated its potential to optimize the detection, localization, characterization and management of small screen-detected nodules leading to earlier diagnosis, more effective therapy, impacting survival of cancer patients.

    Poster présenté au Congrès de l’American Thoracic Society à San Francisco.


    A. Vachani [1], R. Osarogiagbon [2], C. Gotera [3], L. Seijo [4], G. Bastarrika [4], E. Ostrin [5], J. Dennison [5], C. Voyton [6], P. Baudot [6], E. Geremia [6], P. Siot [6], V. Le [6], B. Huet [6], V. Bourdes [6], – Affiliations: [1] Penn Medicine, Philadelphia, PA, USA, [2] Baptist Cancer Center, Memphis, TN, USA, [3] Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain, [4] Clínica Universidad de Navarra, Madrid, Spain, [5] MD Anderson Cancer Center, Houston, TX, USA, [6] Median Technologies, Valbonne, France.
    Téléchargement Median_eyonisLCS_ATS_2025_poster_V01_Draft02_External_Review-1.pdf
  • 05/13/2025

    Budget impact model of enhanced Lung Cancer Screening with AI/ML tech-based software as a medical device (SaMD) on a us cohort and private payer perspective

    Lung cancer remains a leading cause cancer death worldwide. Current standard of care, screening based on Low-Dose Computed Tomography (LDCT), has improved early detection, yet false positives and late-stage diagnoses persist. CADe/CADx SaMD enables earlier lung cancer detection & characterization, reduces invasive and useless procedures, and delivers meaningful cost savings for US payers. These findings advocate for integrating CADe/CADx SaMD into routine lung cancer screening programs.

    Poster présenté au Congrès ISPOR US 2025 à Montréal, Canada.


    A. Disset [1], C. Voyton [1], D. Quach [2], [3], E. Lam [3], – Affiliations: [1] Median Technologies, Valbonne, France. [2] Pharmacy Systems, Outcomes, and Policy, University of Illinois at Chicago College of Pharmacy, Chicago, IL, USA, [3] Avania, USA
    Téléchargement 20250425_ISPOR-POSTER-Montreal-16052025.pdf
  • 03/28/2025

    AI/ML-based lung cancer detection and characterization for lung cancer screening: results from the REALITY study on early-stage lung cancer

    Tumor size and cancer stage at diagnosis are key determinants of survival: small, early-stage cancers are more responsive to treatment, with better prognoses. The AI/ML-Based CADe/CADx showed high performance in detecting early lung cancer, high sensitivity for cancers of different stage, T-category and cell type, excellent cancer recall rate (>96.5%) on all cancer stages and t-categories. This CADe/CADx can assist in the accurate follow-up of suspicious lung nodules, optimizing the management of patients in lung cancer screening.

    Présenté au European Lung Cancer Congress à Paris.


    R. Osarogiagbon [1], A. Vachani [2], C. Gotera [3], L. Seijo [4], G. Bastarrika [4], E. Ostrin [5], J. Dennison [5], C. Voyton [6], P. Baudot [6], E. Geremia [6], P. Siot [6], V. Le [6], B. Renoust [6], B. Huet [6], V. Bourdes [6], – Affiliations: [1] Baptist Cancer Center, Memphis, TN, USA, [2] Penn Medicine, Philadelphia, PA, USA, [3] Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain, [4] Clínica Universidad de Navarra, Madrid, Spain, [5] MD Anderson Cancer Center, Houston, TX, USA, [6] Median Technologies, Valbonne, France.
    Téléchargement Median_eyonisLCS_ELCC_2025_AIML-BASED-LUNG-CANCER-DETECTION-AND-CHARACTERIZATION-FOR-LCS.pdf
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