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

Highlights•DTI-derived tissue infiltration maps may improve radiotherapy planning. •Infiltrative tumor volumes overlapped frequently with observed recurrences. •An infiltrative tumor CTV showed a volume size in between CTV RTOG and CTV EORTC. •An infiltrative tumor CTV with a reduced margin may be u...

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Veröffentlicht in:	Radiotherapy and oncology 2019-09, Vol.138, p.166-172
Hauptverfasser:	Peeken, Jan C, Molina-Romero, Miguel, Diehl, Christian, Menze, Bjoern H, Straube, Christoph, Meyer, Bernhard, Zimmer, Claus, Wiestler, Benedikt, Combs, Stephanie E
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Aged, 80 and over Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain Neoplasms - radiotherapy Brain Neoplasms - surgery Deep Learning Diffusion tensor imaging Diffusion Tensor Imaging - methods Female Glioblastoma Glioblastoma - diagnostic imaging Glioblastoma - pathology Glioblastoma - radiotherapy Glioblastoma - surgery Hematology, Oncology, and Palliative Medicine Humans Male Middle Aged Neoplasm Recurrence, Local - diagnosis Neoplasm Recurrence, Local - pathology Neoplasm Recurrence, Local - surgery Personalized medicine Precision Medicine - methods Radiotherapy Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted - methods Retrospective Studies Tissue volume maps
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Zusammenfassung:	Highlights•DTI-derived tissue infiltration maps may improve radiotherapy planning. •Infiltrative tumor volumes overlapped frequently with observed recurrences. •An infiltrative tumor CTV showed a volume size in between CTV RTOG and CTV EORTC. •An infiltrative tumor CTV with a reduced margin may be used for dose escalation.
ISSN:	0167-8140 1879-0887
DOI:	10.1016/j.radonc.2019.06.031