Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI

• URL: http://arxiv.org/abs/2511.18208v1
• Date: Sat, 22 Nov 2025 22:44:50 GMT
• Title: Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
• Authors: Ahmed Gomaa, Annette Schwarz, Ludwig Singer, Arnd Dörfler, Matthias Stefan May, Pluvio Stephan, Ishita Sheth, Juliane Szkitsak, Katharina Breininger, Yixing Huang, Benjamin Frey, Oliver Schnell, Daniel Delev, Roland Coras, Daniel Höfler, Philipp Schubert, Jenny Stritzelberger, Sabine Semrau, Andreas Maier, Dieter H Heiland, Udo S. Gaipl, Andrea Wittig, Rainer Fietkau, Christoph Bert, Stefanie Corradini, Florian Putz,
• Abstract summary: Differentiating radiation necrosis from tumor progression after radiosurgery is a critical challenge in brain metastases.<n> Conventional supervised deep learning approaches are constrained by scarce biopsy-confirmed training data.<n>Self-supervised learning overcomes this by leveraging the growing availability of largescale unlabeled brain metastases imaging datasets.
• Score: 3.291383664051985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Differentiating radiation necrosis (RN) from tumor progression after stereotactic radiosurgery (SRS) remains a critical challenge in brain metastases. While histopathology represents the gold standard, its invasiveness limits feasibility. Conventional supervised deep learning approaches are constrained by scarce biopsy-confirmed training data. Self-supervised learning (SSL) overcomes this by leveraging the growing availability of large-scale unlabeled brain metastases imaging datasets. Methods: In a two-phase deep learning strategy inspired by the foundation model paradigm, a Vision Transformer (ViT) was pre-trained via SSL on 10,167 unlabeled multi-source T1CE MRI sub-volumes. The pre-trained ViT was then fine-tuned for RN classification using a two-channel input (T1CE MRI and segmentation masks) on the public MOLAB dataset (n=109) using 20% of datasets as same-center held-out test set. External validation was performed on a second-center test cohort (n=28). Results: The self-supervised model achieved an AUC of 0.916 on the same-center test set and 0.764 on the second center test set, surpassing the fully supervised ViT (AUC 0.624/0.496; p=0.001/0.008) and radiomics (AUC 0.807/0.691; p=0.005/0.014). Multimodal integration further improved performance (AUC 0.947/0.821; p=0.073/0.001). Attention map visualizations enabled interpretability showing the model focused on clinically relevant lesion subregions. Conclusion: Large-scale pre-training on increasingly available unlabeled brain metastases datasets substantially improves AI model performance. A two-phase multimodal deep learning strategy achieved high accuracy in differentiating radiation necrosis from tumor progression using only routine T1CE MRI and standard clinical data, providing an interpretable, clinically accessible solution that warrants further validation.

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