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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)

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Open Access Green as soon as Postprint is submitted to ZB.

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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.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Mri ; Brain Metastasis ; External Testing ; Neural Network ; Stereotactic Radiotherapy

ISSN (print) / ISBN 0167-8140

e-ISSN 1879-0887

Journal Radiotherapy and Oncology

Quellenangaben Volume: 178, Article Number: 109425

Publisher Elsevier

Publishing Place Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Radiation Medicine (IRM)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)

Grants
Bangerter-Rhyner Foundation
Young Talents in Clinical Research Beginners Grant from the Swiss Academy of Medical Sciences (SAMW)
Deutsche Forschungsgemeinschaft (DFG, German Research)