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Titel:

Deep learning derived tumor infiltration maps for personalized target definition in Glioblastoma radiotherapy.

Dokumenttyp:
Journal Article
Autor(en):
Peeken, Jan C; Molina-Romero, Miguel; Diehl, Christian; Menze, Bjoern H; Straube, Christoph; Meyer, Bernhard; Zimmer, Claus; Wiestler, Benedikt; Combs, Stephanie E
Abstract:
PURPOSE: Glioblastoma is routinely treated by concomitant radiochemotherapy. Current target definition guidelines use anatomic MRI (magnetic resonance imaging) scans, taking into account contrast enhancement and the rather unspecific hyperintensity on the fluid-attenuated inversion recovery (FLAIR) sequence. METHODS AND MATERIALS: We applied deep learning based free water correction of diffusion tensor imaging (DTI) scans to estimate the infiltrative gross tumor volume (iGTV) inside of the FLAIR...     »
Zeitschriftentitel:
Radiother Oncol
Jahr:
2019
Band / Volume:
138
Seitenangaben Beitrag:
166-172
Volltext / DOI:
doi:10.1016/j.radonc.2019.06.031
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/31302391
Print-ISSN:
0167-8140
TUM Einrichtung:
Fachgebiet Neuroradiologie (Prof. Zimmer); Klinik und Poliklinik für RadioOnkologie und Strahlentherapie; Neurochirurgische Klinik und Poliklinik
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