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An innovative AI tool, developed by Harvard Medical School, is set to transform the landscape of cancer diagnosis and treatment. With the ability to analyze digitized tumor tissue, the AI model, called CHIEF, can predict patient response to treatment, improve treatment choice and offer accurate prediction of survival in 19 different types of cancer. This approach, described in Nature on Sept. 4, represents a significant advance over current AI models, which are limited to specific tasks and a small number of cancer types. 

Key points:

•  CHIEF uses digital images of tumor tissue to improve cancer diagnosis and treatment.
•  The model has been tested on 19 cancer types, demonstrating superior flexibility and accuracy compared to existing AI methods.
•  CHIEF can predict genetic mutations relevant to treatment response and identify patients who might benefit from personalized therapies.
•  Its effectiveness has been validated on a wide range of samples from international institutions.

CHIEF uses features of the tumor microenvironment to accurately identify cancer cells, the tumor’s molecular profile, and possible genetic mutations, surpassing traditional methods. Trained on 15 million unlabeled images and further refined on 60,000 tissue images from different organs, CHIEF has been tested on more than 19,400 images from 32 international datasets, demonstrating superior performance in cancer detection and prediction of clinical outcomes. CHIEF’s ability to identify cancer cells and predict the efficacy of therapies has been validated on a wide range of samples, regardless of the digitization technique or method of cancer cell collection. This makes it a versatile model that can be adapted to different clinical situations. In addition, CHIEF can rapidly identify cellular patterns that suggest specific genomic variations, offering a possible alternative to the expensive and time-consuming process of DNA sequencing. In comparative tests, CHIEF achieved 94 percent accuracy in cancer detection and outperformed existing methods by 36 percent in tasks such as predicting survival and identifying genes linked to treatment response. Among the model’s findings were that tumors with higher numbers of immune cells in long-term survivors show a better response, while other atypical cell characteristics are correlated with poorer survival. This model holds promise for optimizing the identification of patients who might benefit from targeted experimental treatments, reducing reliance on ineffective standard therapies.

CHIEF’s approach opens new perspectives for more personalized and timely cancer management.