AI automated tumor response assessment

  1. AI-discovered early endpoint

    Accelerating time to approval

  2. AI-driven statistical adjustment

    Smaller sample size without reducing power

  3. AI-powered dose optimization

    Maximizing efficacy while minimizing toxicity

  4. Trial-matched real-world evidence

    Well-defined and reliable outcome assessments

Case study

  • ESMO 2023 | Clinical trial data| In collaboration with Pfizer

    ESMO 2023 | Clinical trial data| In collaboration with Pfizer

    1421P AI-powered intracranial tumor response predicts systemic progression with high concordance in endpoint evaluation in the phase III CROWN study

    Imaging reading for endpoint evaluation in clinical trials presents challenges in reproducibility and accuracy, limiting the use of novel endpoints beyond human capabilities. This analysis utilized AI to perform intracranial tumor response assessments and explored potential novel endpoints derived from lesion-level insights. The study demonstrated that the AI, VBrain, closely aligned with independent readers' assessments. It found that a novel AI-powered endpoint, early tumor shrinkage (ETS) across all brain lesions rather than just target lesions, could be a more predictive measure of treatment efficacy in ALK-positive advanced NSCLC patients."

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  • SNO/ASCO 2023 | Real-world data| In collaboration with Stanford Medicine

    SNO/ASCO 2023 | Real-world data| In collaboration with Stanford Medicine

    SDPS-09 MIXED RESPONSE OF BRAIN METASTASES TREATED WITH OSIMERTINIB PREDICTS INFERIOR SURVIVAL OUTCOMES: AN ARTIFICIAL INTELLIGENCE-BASED LESION-LEVEL ASSESSMENT FOR CRANIAL CONTROL OF EGFRMUTANT NONSMALL CELL LUNG CANCER

    This study demonstrates that MiR in brain metastases from EGFR-mutant NSCLC treated with osimertinib is associated with inferior survival outcomes and a higher risk of local progression. Lesion-level response assessment using AI may provide important prognostic information and aid in treatment decision-making for these patients. *Mixed response (MiR) was defined as the occurrence of progressive or new lesions along with synchronous responsive shrinking intracranial lesions at the first follow-up scan.

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