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| Home > Publications database > Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study. > print |
| 001 | 302814 | ||
| 005 | 20250720021401.0 | ||
| 024 | 7 | _ | |a 10.1016/j.radonc.2025.111031 |2 doi |
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| 037 | _ | _ | |a DKFZ-2025-01354 |
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| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Erdur, Ayhan Can |b 0 |
| 245 | _ | _ | |a Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1752657850_10334 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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.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.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.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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| 650 | _ | 7 | |a Artificial Intelligence |2 Other |
| 650 | _ | 7 | |a Brain metastases |2 Other |
| 650 | _ | 7 | |a Stereotactic radiotherapy |2 Other |
| 650 | _ | 7 | |a Vision Transformers |2 Other |
| 700 | 1 | _ | |a Scholz, Daniel |b 1 |
| 700 | 1 | _ | |a Nguyen, Q Mai |b 2 |
| 700 | 1 | _ | |a Buchner, Josef A |b 3 |
| 700 | 1 | _ | |a Mayinger, Michael |b 4 |
| 700 | 1 | _ | |a Christ, Sebastian M |b 5 |
| 700 | 1 | _ | |a Brunner, Thomas B |b 6 |
| 700 | 1 | _ | |a Wittig, Andrea |b 7 |
| 700 | 1 | _ | |a Zimmer, Claus |b 8 |
| 700 | 1 | _ | |a Meyer, Bernhard |b 9 |
| 700 | 1 | _ | |a Guckenberger, Matthias |b 10 |
| 700 | 1 | _ | |a Andratschke, Nicolaus |b 11 |
| 700 | 1 | _ | |a El Shafie, Rami A |b 12 |
| 700 | 1 | _ | |a Debus, J Urgen |b 13 |
| 700 | 1 | _ | |a Rogers, Susanne |b 14 |
| 700 | 1 | _ | |a Riesterer, Oliver |b 15 |
| 700 | 1 | _ | |a Schulze, Katrin |b 16 |
| 700 | 1 | _ | |a Feldmann, Horst J |b 17 |
| 700 | 1 | _ | |a Blanck, Oliver |b 18 |
| 700 | 1 | _ | |a Zamboglou, Constantinos |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Bilger-Z, Angelika |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Grosu, Anca L |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Wolff, Robert |b 22 |
| 700 | 1 | _ | |a Eitz, Kerstin A |0 P:(DE-HGF)0 |b 23 |
| 700 | 1 | _ | |a Combs, Stephanie E |0 P:(DE-HGF)0 |b 24 |
| 700 | 1 | _ | |a Bernhardt, Denise |0 P:(DE-HGF)0 |b 25 |
| 700 | 1 | _ | |a Wiestler, Benedikt |b 26 |
| 700 | 1 | _ | |a Rueckert, Daniel |b 27 |
| 700 | 1 | _ | |a Peeken, Jan C |0 P:(DE-HGF)0 |b 28 |
| 773 | _ | _ | |a 10.1016/j.radonc.2025.111031 |g Vol. 210, p. 111031 - |0 PERI:(DE-600)1500707-8 |p 111031 |t Radiotherapy and oncology |v 210 |y 2025 |x 0167-8140 |
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