PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Buchner, J.A.* ; Kofler, F. ; Etzel, L.* ; Mayinger, M.* ; Christ, S.M.* ; Brunner, T.B.* ; Wittig, A.* ; Menze, B.* ; Zimmer, C.* ; Meyer, B.* ; Guckenberger, M.* ; Andratschke, N.* ; El Shafie, R.A.* ; Debus, J.* ; Rogers, S.* ; Riesterer, O.* ; Schulze, K.* ; Feldmann, H.J.* ; Blanck, O.* ; Zamboglou, C.* ; Ferentinos, K.* ; Wolff, R.* ; Eitz, K.A. ; Combs, S.E. ; Bernhardt, D.* ; Wiestler, B.* ; Peeken, J.C.

Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study.

Radiother. Oncol. 178:109425 (2022)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
BACKGROUND: Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS: We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n=88) including four centers. RESULTS: Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION: Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Altmetric
6.901
0.000
4
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Mri ; Brain Metastasis ; External Testing ; Neural Network ; Stereotactic Radiotherapy
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Zeitschrift Radiotherapy and Oncology
Quellenangaben Band: 178, Heft: , Seiten: , Artikelnummer: 109425 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)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30203 - Molecular Targets and Therapies
30205 - Bioengineering and Digital Health
Forschungsfeld(er) Radiation Sciences
Enabling and Novel Technologies
PSP-Element(e) G-501300-001
G-530001-001
Förderungen
Bangerter-Rhyner Foundation
Young Talents in Clinical Research Beginners Grant from the Swiss Academy of Medical Sciences (SAMW)
Deutsche Forschungsgemeinschaft (DFG, German Research)
DOI 10.1016/j.radonc.2022.11.014
WOS ID 000920358700003
Scopus ID 85143497833
PubMed ID 36442609
Erfassungsdatum 2022-12-09
[➜Korrektur melden]