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

Complete list of metadata

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 : Thursday, January 14, 2021 - 11:25:07 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?

File

Identifiers

Collections

Citation

Export

Metrics

127

72

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

File

Identifiers

Collections

Citation

Export

Share

Metrics

127

72