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Titel:

Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.

Dokumenttyp:
Journal Article
Autor(en):
Peeken, Jan C; Hesse, Josefine; Haller, Bernhard; Kessel, Kerstin A; Nüsslin, Fridtjof; Combs, Stephanie E
Abstract:
BACKGROUND: For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in...     »
Zeitschriftentitel:
Strahlenther Onkol
Jahr:
2018
Band / Volume:
194
Heft / Issue:
6
Seitenangaben Beitrag:
580-590
Sprache:
eng
Volltext / DOI:
doi:10.1007/s00066-018-1276-4
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/29442128
Print-ISSN:
0179-7158
TUM Einrichtung:
Institut für Medizinische Statistik und Epidemiologie; Klinik und Poliklinik für RadioOnkologie und Strahlentherapie
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