PuSH - Publikationsserver des Helmholtz Zentrums München

Lipkova, J.* ; Angelikopoulos, P.* ; Wu, S.* ; Alberts, E.* ; Wiestler, B.* ; Diehl, C.* ; Preibisch, C.* ; Pyka, T.* ; Combs, S.E. ; Hadjidoukas, P.* ; van Leemput, K.* ; Koumoutsakos, P.* ; Lowengrub, J.* ; Menze, B.*

Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans, and bayesian inference.

IEEE Trans. Med. Imaging 38, 1875-1884 (2019)
Verlagsversion Preprint DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Glioblastoma ; Radiotherapy Planning ; Bayesian Inference ; Fet-pet ; Multimodal Medical Scans; Proliferation; Neurooncology; Multiforme; Pet
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Quellenangaben Band: 38, Heft: 8, Seiten: 1875-1884 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed