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Scientific publications

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  • 08/22/2023

    Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer


    M. Felfli [1], Y. Liu [1], Zerka [1], C. Voyton [1], A. Thinnes [1], S. Jacques [1], A. Iannessi [1], F. S. Bodard [2] – Affiliations: [1] Median Technologies, Valbonne, France. [2] Hôpital Universitaire Necker, Paris, France
    Read more Download ijms-24-11433.pdf
  • 06/02/2023

    Multicenter Evaluation of AI-Based CT Radiomics for EGFR Mutation Prediction in NSCLC

    This abstract discusses using CT image-based radiomics model as a non-invasive solution to predict EGFR mutation status in NSCLC. The study collected CT images from multiple centers and open-source databases to investigate the performance of the model. The model achieved promising results with an AUC of 0.83 on cross-validation and an AUC of 0.76 on the test set. The authors conclude that AI-powered medical image analysis has the potential to serve as predictive biomarkers for guiding targeted therapies in the future.


    Y. Liu [1], F. Zerka [1], S. Bodard [2], M. Felfli [1], C. Voyton [1], A. Thinnes [1], S. Jacques [1], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France. [2] Hôpital Universitaire Necker, Paris, France
    Read more Download Multicenter-evaluation-of-AI-based-CT-radiomics-for-EGFR-mutation-prediction-in-NSCLC.pdf
  • 06/02/2023

    CT Based Radiomics Signature for Phenotyping Histopathological Subtype in Patients With Non-Small Cell Lung Cancer

    The study in this abstract aimed to use a CT-based radiomics model to predict the histopathological subtype of non-small cell lung cancer (NSCLC) patients. The study included 678 patients, with 531 used for training and 147 for testing. The robust radiomics features extracted from the CT scans were used to train a support vector machine (SVM) classifier, which achieved an accuracy of 0.80 on the training set and 0.77 on the test set. The study showed that CT-based radiomics can accurately predict the histopathology subtype of NSCLC patients, offering a less invasive and more cost-effective alternative to traditional tissue analysis methods.


    Y. Liu [1], F. Zerka [1], S. Bodard [2], M. Felfli [1], C. Voyton [1], A. Thinnes [1], S. Jacques [1], A. Iannessi [1], – Affiliations: [1] Median Technologies, Valbonne, France. [2] Hôpital Universitaire Necker, Paris, France
    Read more Download CT-based-radiomics-signature-for-phenotyping-histopathological-subtype-in-patients-with-non-small-cell-lung-cancer.pdf
  • 05/15/2023

    Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?


    Antoine Iannessi [1], Hubert Beaumont [1] – Affiliations: [1] Median Technologies, Valbonne, France.
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  • 05/11/2023

    Discordance rates on esophagus assessment between Blinded Independent readers applying RECIST 1.1 criteria in the maintenance esophageal cancer trial following definitive chemoradiation therapy


    Y. Liu [1], C. Ojango [1], L. Balcells [1], J. Ching [2], Y. WANG [2], A. Iannessi [1],– Affiliations: [1] Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France. [2] Median Technologies, Shanghai, China.
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