{"ID":2880875,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16650","arxiv_id":"2508.16650","title":"Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence","abstract":"Brain tumour imaging assessment typically requires both pre- and post-contrast MRI, but gadolinium administration is not always desirable, such as in frequent follow-up, renal impairment, allergy, or paediatric patients. We aimed to develop and validate a deep learning model capable of predicting brain tumour contrast enhancement from non-contrast MRI sequences alone. We assembled 11089 brain MRI studies from 10 international datasets spanning adult and paediatric populations with various neuro-oncological states, including glioma, meningioma, metastases, and post-resection appearances. Deep learning models (nnU-Net, SegResNet, SwinUNETR) were trained to predict and segment enhancing tumour using only non-contrast T1-, T2-, and T2/FLAIR-weighted images. Performance was evaluated on 1109 held-out test patients using patient-level detection metrics and voxel-level segmentation accuracy. Model predictions were compared against 11 expert radiologists who each reviewed 100 randomly selected patients. The best-performing nnU-Net achieved 83% balanced accuracy, 91.5% sensitivity, and 74.4% specificity in detecting enhancing tumour. Enhancement volume predictions strongly correlated with ground truth (R2 0.859). The model outperformed expert radiologists, who achieved 69.8% accuracy, 75.9% sensitivity, and 64.7% specificity. 76.8% of test patients had Dice over 0.3 (acceptable detection), 67.5% had Dice over 0.5 (good detection), and 50.2% had Dice over 0.7 (excellent detection). Deep learning can identify contrast-enhancing brain tumours from non-contrast MRI with clinically relevant performance. These models show promise as screening tools and may reduce gadolinium dependence in neuro-oncology imaging. Future work should evaluate clinical utility alongside radiology experts.","short_abstract":"Brain tumour imaging assessment typically requires both pre- and post-contrast MRI, but gadolinium administration is not always desirable, such as in frequent follow-up, renal impairment, allergy, or paediatric patients. We aimed to develop and validate a deep learning model capable of predicting brain tumour contrast...","url_abs":"https://arxiv.org/abs/2508.16650","url_pdf":"https://arxiv.org/pdf/2508.16650v2","authors":"[\"James K Ruffle\",\"Samia Mohinta\",\"Guilherme Pombo\",\"Asthik Biswas\",\"Alan Campbell\",\"Indran Davagnanam\",\"David Doig\",\"Ahmed Hammam\",\"Harpreet Hyare\",\"Farrah Jabeen\",\"Emma Lim\",\"Dermot Mallon\",\"Stephanie Owen\",\"Sophie Wilkinson\",\"Sebastian Brandner\",\"Parashkev Nachev\"]","published":"2025-08-19T21:22:47Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"q-bio.QM\"]","methods":"[]","has_code":false}