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Erdur, A.C.* ; Scholz, D.* ; Nguyen, Q.M.* ; Buchner, J.A.* ; Mayinger, M.* ; Christ, S.M.* ; Brunner, T.B.* ; Wittig, A.* ; Zimmer, C.* ; Meyer, B.* ; Guckenberger, M.* ; Andratschke, N.* ; El Shafie, R.A.* ; Debus, J.U.* ; Rogers, S.* ; Riesterer, O.* ; Schulze, K.* ; Feldmann, H.J.* ; Blanck, O.* ; Zamboglou, C.* ; Bilger-Z, A.* ; Grosu, A.L.* ; Wolff, R.* ; Eitz, K.A. ; Combs, S.E. ; Bernhardt, D.* ; Wiestler, B.* ; Rueckert, D.* ; Peeken, J.C.

Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study.

Radiother. Oncol. 210:111031 (2025)

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BACKGROUND AND PURPOSE: This study investigates the use of Vision Transformers (ViTs) to predict Freedom from Local Failure (FFLF) in patients with brain metastases using pre-operative MRI scans. The goal is to develop a model that enhances risk stratification and informs personalized treatment strategies. MATERIALS AND METHODS: Within the AURORA retrospective trial, patients (n = 352) who received surgical resection followed by post-operative stereotactic radiotherapy (SRT) were collected from seven hospitals. We trained our ViT for the direct image-to-risk task on T1-CE and FLAIR sequences and combined clinical features along the way. We employed segmentation-guided image modifications, model adaptations, and specialized patient sampling strategies during training. The model was evaluated with five-fold cross-validation and ensemble learning across all validation runs. An external, international test cohort (n = 99) within the dataset was used to assess the generalization capabilities of the model, and saliency maps were generated for explainability analysis. RESULTS: We achieved a competent C-Index score of 0.7982 on the test cohort, surpassing all clinical, CNN-based, and hybrid baselines. Kaplan-Meier analysis showed significant FFLF risk stratification. Saliency maps focusing on the BM core confirmed that model explanations aligned with expert observations. CONCLUSIO: Our ViT-based model offers a potential for personalized treatment strategies and follow-up regimens in patients with brain metastases. It provides an alternative to radiomics as a robust, automated tool for clinical workflows, capable of improving patient outcomes through effective risk assessment and stratification.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Artificial Intelligence ; Brain Metastases ; Stereotactic Radiotherapy ; Vision Transformers

ISSN (print) / ISBN 0167-8140

e-ISSN 1879-0887

Journal Radiotherapy and Oncology

Quellenangaben Volume: 210, Article Number: 111031

Publisher Elsevier

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Radiation Medicine (IRM)