The new approach marks a major step forward in the design of AI tools to support clinical decisions in cancer diagnosis, therapy. The model uses features of a tumor's microenvironment to forecast how a patient might respond to therapy and to help inform individualized treatments.
The new approach marks a major step forward in the design of AI tools to support clinical decisions in cancer diagnosis, therapy. The model uses features of a tumor's microenvironment to forecast how a patient might respond to therapy and to help inform individualized treatments. The model can expedite the identification of patients not likely to benefit from standard treatments used in some forms of cancer.
While other foundation AI models for medical diagnosis based on pathology images have emerged recently, this is believed to be the first to predict patient outcomes and validate them across several international patient groups. The findings, the research team said, add to growing evidence that AI-powered approaches can enhance clinicians' ability to evaluate cancers efficiently and accurately, including the identification of patients who might not respond well to standard cancer therapies.
Following training, the team tested CHIEF's performance on more than 19,400 whole-slide images from 32 independent datasets collected from 24 hospitals and patient cohorts across the globe. CHIEF outperformed current AI methods for predicting genomic variations in a tumor by looking at the microscopic slides. This new AI approach successfully identified features associated with several important genes related to cancer growth and suppression, and it predicted key genetic mutations related to how well a tumor might respond to various standard therapies.
Exposing the model to more molecular data to enhance its ability to identify cancers with different levels of aggressiveness
Brain Tumor Colon Cancer Breast Cancer Computer Modeling Computational Biology Mathematical Modeling Photography
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