Crossmark

Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients

Crossref DOI link: https://doi.org/10.1007/s00066-018-1294-2

Published Online: 2018-03-20

Published Print: 2018-09

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Peeken, Jan C.

Goldberg, Tatyana

Knie, Christoph

Komboz, Basil

Bernhofer, Michael

Pasa, Francesco

Kessel, Kerstin A.

Tafti, Pouya D.

Rost, Burkhard

Nüsslin, Fridtjof

Braun, Andreas E.

Combs, Stephanie E.
Funding

Funding for this research was provided by:

Deutsches Konsortium für Translationale Krebsforschung

Alexander von Humboldt Foundation

Allianz SE

Allianz SE

Allianz SE

Allianz SE

Alexander von Humboldt-Stiftung

Deutsches Konsortium für Translationale Krebsforschung

Deutsches Konsortium für Translationale Krebsforschung
License Information

Text and Data Mining valid from 2018-03-20
More Information

Article History

Received: 5 October 2017

Accepted: 5 March 2018

First Online: 20 March 2018

Compliance with ethical guidelines

:

: J.C. Peeken, T. Goldberg, C. Knie, B. Komboz, M. Bernhofer, F. Pasa, K.A. Kessel, P.D. Tafti, B. Rost, F. Nüsslin, A.E. Braun, and S.E. Combs declare that they have no competing interests.

: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.