Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations

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

doi:10.1016/j.canrad.2021.06.027

Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations

Titre traduit de la contribution: Intelligence artificielle en radiothérapie: radiomique, pathomique, et prédiction de la survie et de la réponse aux traitements

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

Résultats de recherche: Contribution à un journal › Article 'review' › Revue par des pairs

3 Citations (Scopus)

Résumé

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.

Titre traduit de la contribution	Intelligence artificielle en radiothérapie: radiomique, pathomique, et prédiction de la survie et de la réponse aux traitements
langue originale	Anglais
Pages (de - à)	630-637
Nombre de pages	8
journal	Cancer/Radiotherapie
Volume	25
Numéro de publication	6-7
Les DOIs	https://doi.org/10.1016/j.canrad.2021.06.027
état	Publié - 1 oct. 2021

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10.1016/j.canrad.2021.06.027

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Sun, R., Lerousseau, M., Henry, T., Carré, A., Leroy, A., Estienne, T., Niyoteka, S., Bockel, S., Rouyar, A., Alvarez Andres, A., Benzazon, N., Battistella, E., Classe, M., Robert, C., Scoazec, J. Y., & Deutsch (2021). Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations. Cancer/Radiotherapie, 25(6-7), 630-637. https://doi.org/10.1016/j.canrad.2021.06.027

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title = "Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations",

abstract = "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.",

keywords = "Artificial intelligence, Dosiomics, Pathomics, Radiomics, Radiotherapy",

author = "R. Sun and M. Lerousseau and T. Henry and A. Carr{\'e} and A. Leroy and T. Estienne and S. Niyoteka and S. Bockel and A. Rouyar and {Alvarez Andres}, Andres and N. Benzazon and E. Battistella and M. Classe and C. Robert and Scoazec, {J. Y.} and Deutsch",

note = "Publisher Copyright: {\textcopyright} 2021 Soci{\'e}t{\'e} fran{\c c}aise de radioth{\'e}rapie oncologique (SFRO)",

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Sun, R, Lerousseau, M, Henry, T, Carré, A, Leroy, A, Estienne, T, Niyoteka, S, Bockel, S, Rouyar, A, Alvarez Andres, A, Benzazon, N, Battistella, E, Classe, M, Robert, C, Scoazec, JY & Deutsch 2021, 'Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations', Cancer/Radiotherapie, VOL. 25, Numéro 6-7, p. 630-637. https://doi.org/10.1016/j.canrad.2021.06.027

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AU - Sun, R.

AU - Lerousseau, M.

AU - Henry, T.

AU - Carré, A.

AU - Leroy, A.

AU - Estienne, T.

AU - Niyoteka, S.

AU - Bockel, S.

AU - Rouyar, A.

AU - Alvarez Andres, Andres

AU - Benzazon, N.

AU - Battistella, E.

AU - Classe, M.

AU - Robert, C.

AU - Scoazec, J. Y.

AU - Deutsch,

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AB - 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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