Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914313148563456 |
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| author | Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev |
| author_facet | Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence Ruffle, James K Mohinta, Samia Pombo, Guilherme Biswas, Asthik Campbell, Alan Davagnanam, Indran Doig, David Hammam, Ahmed Hyare, Harpreet Jabeen, Farrah Lim, Emma Mallon, Dermot Owen, Stephanie Wilkinson, Sophie Brandner, Sebastian Nachev, Parashkev Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2508.16650 |