Improving risk assessment of local failure in brain metastases patients using vision transformers: a multicentric development and validation study - Erdur, Ayhan Can; Scholz, Daniel; Nguyen, Q. Mai; Buchner, Josef; Mayinger, Michael Christian; Christ, Sebastian M.; Brunner, Thomas B.; Wittig, Andrea; Zimmer, Claus; Meyer, Bernhard; Guckenberger, Matthias; Andratschke, Nicolaus; et al. (Radiotherapy and oncology, 2025)
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. - Conclusion - 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.
Gesehen am 17.12.2025