How to find the best radiomics features for prediction of overall survival in SBRT for HCC?

P. Fontaine; Oscar Acosta; F. Riet; J. Castelli; K. Gnep; A. Simon; A. Depeursinge; R. de Crevoisier

doi:10.1016/S0167-8140(19)31385-4

Journal articles

How to find the best radiomics features for prediction of overall survival in SBRT for HCC?

P. Fontaine ^{1, 2} Oscar Acosta ^{1, 2} F. Riet ^{1, 2} J. Castelli ^{1, 2} K. Gnep ^{1, 2} A. Simon ^{1, 2} A. Depeursinge ³ R. de Crevoisier ^{1, 2}

1 LTSI - Laboratoire Traitement du Signal et de l'Image

2 CRLCC - CRLCC Eugène Marquis

3 HES-SO - University of Applied Sciences and Arts of Western Switzerland

Document type :

Journal articles

Domain :

Life Sciences [q-bio] / Bioengineering

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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02177168
Contributor : Laurent Jonchère Connect in order to contact the contributor
Submitted on : Friday, July 10, 2020 - 10:30:10 AM
Last modification on : Wednesday, September 14, 2022 - 10:20:04 AM
Long-term archiving on: : Monday, November 30, 2020 - 7:25:10 PM

How to find the best radiomics features for prediction of overall survival in SBRT for HCC?

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How to find the best radiomics features for prediction of overall survival in SBRT for HCC?

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