Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations. Intelligence artificielle en radiothérapie : radiomique, pathomique, et prédiction de la survie et de la réponse aux traitements

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Sun, R. | Lerousseau, M. | Henry, T. | Carré, A. | Leroy, A. | Estienne, T. | Niyoteka, S. | Bockel, S. | Rouyar, A. | Alvarez Andres, É. | Benzazon, N. | Battistella, E. | Classe, M. | Robert, C. | Scoazec, J.Y. | Deutsch, É.

Edité par CCSD ; Elsevier Masson -

International audience. Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.

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