Insights Scientific publications

Scientific publications

  • 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


    R. OSAROGIAGBON, A. VACHANI, C. GOTERA, L. SEIJO, G. BASTARRIKA, E. OSTRIN, J. DENNISON, C. VOYTON, P. BAUDOT, E. GEREMIA, P. SIOT, V. LE, RENOUST, B. HUET, V. BOURDES
    Download Median_eyonisLCS_ELCC_2025_AIML-BASED-LUNG-CANCER-DETECTION-AND-CHARACTERIZATION-FOR-LCS.pdf
  • 02/24/2025

    Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study


    H. Beaumont[1], A. Iannessi[1], A. Thinnes [1], S. Jacques[1], A. Quintás-Cardama [2], – Affiliations: [1] Median Technologies, Valbonne, France; [2] TCR2 Therapeutics, Cambridge, MA, USA.
    Read more Download cancers-17-00463.pdf
  • 12/20/2024

    Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics

    This study introduces an innovative way to predict clinical outcomes in non-small cell lung cancer (NSCLC) patients receiving immunotherapy by modeling early tumor growth dynamics with the Gompertz model alongside RECIST 1.1 criteria.


    M. Felfli [1], A. Thinnes [1], S. Jacques [1], Y. Liu [1], A. Iannessi [1,2] – Affiliations: [1] Median Technologies, Valbonne, France. [2] Centre Antoine Lacassagne, Nice, France.
    Read more Download 8_Assessing-immunotherapy-response-going-beyond-RECIST-by-integrating-early-tumor-growth-kinetics.pdf
  • 10/24/2024

    The ins and outs of errors in oncology imaging: the DAC framework for radiologists

    In oncology, the seriousness of the disease amplifies the visibility of radiological errors, leading to both significant individual consequences and broader public health concerns. By leveraging quantitative approaches, the authors reframe the diagnostic process in radiology as a classification problem, a perspective aligned with recent neurocognitive theories on decision-making errors.

    This structured model offers a practical framework for conducting root cause analysis of diagnostic errors in radiology and developing effective risk-management strategies.


    A. Ianessi, H. Beaumont, C. Aguillera, F. Nicol, A-S. Bertrand.
    Read more Download fonc-1-1402838-1.pdf
  • 08/22/2024

    RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads

    The article reviews the variability in radiologic oncology assessments, particularly focusing on RECIST 1.1 criteria, which standardize evaluations to improve consistency and accuracy. It discusses how variability arises from factors like radiologist expertise, image quality, and lesion selection, and emphasizes the importance of standardized protocols and training to mitigate these issues. By addressing the root causes of variability, the article aims to enhance the precision of response assessments, ultimately leading to better patient care and clinical outcomes.


    A. Ianessi, H. Beaumont, C. Ojango, A-S. Bertrand, Y. Liu
    Read more Download PictorialRECIST_2024.pdf