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Title:

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Peeken, Jan C; Goldberg, Tatyana; Pyka, Thomas; Bernhofer, Michael; Wiestler, Benedikt; Kessel, Kerstin A; Tafti, Pouya D; Nüsslin, Fridtjof; Braun, Andreas E; Zimmer, Claus; Rost, Burkhard; Combs, Stephanie E
Abstract:
BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated. METHODS: One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatmen...     »
Journal title abbreviation:
Cancer Med
Year:
2019
Journal volume:
8
Journal issue:
1
Pages contribution:
128-136
Fulltext / DOI:
doi:10.1002/cam4.1908
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/30561851
Print-ISSN:
2045-7634
TUM Institution:
Fachgebiet Neuroradiologie (Prof. Zimmer); Klinik und Poliklinik für RadioOnkologie und Strahlentherapie
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