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Peeken, J.C. ; Molina-Romero, M.* ; Diehl, C.* ; Menze, B.H.* ; Straube, C.* ; Meyer, B.* ; Zimmer, C.* ; Wiestler, B.* ; Combs, S.E.

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

Radiother. Oncol. 138, 166-172 (2019)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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 hyperintense region. We analyzed the resulting iGTVs and their impact on target volume definition in a retrospective cohort of 33 GBM patients.Results: iGTVs were significantly smaller compared to standard pre-and post-operative gross tumor volume (GTV) definitions. Two novel infiltrative tumor GTVs (nGTV(PRE-OP) and nGTV(POST-OP)) defined as the conjunction volume of the standard GTV and the iGTV showed only a moderate increase in size compared to standard GTV definitions. On postoperative scans, the iGTV was predominantly covered by the two clinical target volume (CTV) concepts CTVEORTC and CTVROTG1. A novel infiltrative tumor CTV (nCTV) [nGTV(POST-OP) + 2 cm margin] was significantly smaller compared to CTVROTG1 but larger than CTVEORTC. The overlap volume and conformity index demonstrated a distinct spatial configuration of the nCTV. Tumor recurrences overlapped with the iGTV in all but one patients and were completely covered by the nCTV in all patients. After reducing the margin to 1 cm recurrences coverage was at least in-field in all patients.Conclusion: To conclude, free water corrected DTI scans may help to define infiltrative tumor areas of GBM that could ultimately be used to individualize RT treatment planning in terms of dose sparing or dose escalation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Glioblastoma ; Deep Learning ; Radiotherapy ; Personalized Medicine ; Diffusion Tensor Imaging ; Tissue Volume Maps; Newly-diagnosed Glioblastoma; Free-water Elimination; Randomized Phase-iii; High-grade Gliomas; Adjuvant Temozolomide; Radiation-therapy; Fet-pet; Multiforme; Patterns; Failure
Sprache
Veröffentlichungsjahr 2019
HGF-Berichtsjahr 2019
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Zeitschrift Radiotherapy and Oncology
Quellenangaben Band: 138, Heft: , Seiten: 166-172 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Radiation Sciences
PSP-Element(e) G-501300-001
DOI 10.1016/j.radonc.2019.06.031
WOS ID WOS:000482210600024
PubMed ID 31302391
Erfassungsdatum 2019-08-01
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