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Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective,
James K Ruffle1,2, Samia Mohinta1, Guilherme Pombo3
1Department of Translational Neuroscience and Stroke, Queen Square Institute of Neurology, University College London, London, UK.
Background:
Brain tumour imaging assessment typically requires both pre-contrast and post-contrast MRI, but gadolinium administration is not always desirable-for example, in patients requiring frequent follow-up, or those with renal impairment or allergy, or those who are of pediatric age [0-18 years]. We aimed to develop and validate a deep learning model to predict brain tumour contrast enhancement from non-contrast MRI sequences alone.
Methods:
In this multicohort, retrospective study, we compiled 11 089 brain MRI studies (acquired in 2006-24) from ten datasets across four countries (the UK, the USA, the Netherlands, and Nigeria) and three continents (North America, Europe, and Africa), spanning adult and paediatric populations with no age restriction, and including glioma, meningioma, metastases, and pre-treatment and post-treatment appearances. Three deep learning architectures (nnU-Net, SegResNet, and SwinUNETR) were trained to detect and segment enhancing tumour tissue from T1-weighted (T1w), T2-weighted (T2w), and T2/fluid-attenuated inversion recovery (FLAIR) sequences alone. Performance was assessed in a held-out test set of 1109 studies, with patient-level enhancement detection as the primary endpoint and voxel-level Dice score as the secondary endpoint. Eleven expert radiologists performed the same novel task on a 564-case subset, each reviewing 100 cases while being blinded to clinical history, previous imaging, and referral indication.
Findings:
The best performing model, nnU-Net, reached a balanced accuracy of 83·0% (95% CI 79·1-87·2; sensitivity 91·5% [89·9-93·1], specificity 74·4% [66·7-82·5]) for enhancement detection, with R2=0·859 for enhancement-volume prediction. Among the enhancing cases, 762 (77%) of 992 reached a Dice score of greater than or equal to 0·3, 670 (68%) of 992 had a Dice score of greater than or equal to 0·5, and 498 (50%) of 992 showed a Dice score of greater than or equal to 0·7. In the 564 matched cases, the radiologists' majority-vote performance was lower, with a balanced accuracy of 71·7% (sensitivity 77·6%, specificity 65·8%) under the same blinded conditions. Patient-level enhancement detection was generally consistent across pathologies (proportion of patients reaching voxel-level Dice ≥0·3: meningioma, 93 [93%] of 100; presurgical glioma, 526 [76%] of 691; metastases, 52 [74%] of 70; post-treatment glioma, 82 [74%] of 111), but was lower for paediatric cases (nine [45%] of 20).
Interpretation:
Deep learning can identify contrast-enhancing brain tumours from non-contrast MRI. These models show promise as a triage or decision-support aid-for example, flagging studies in which post-contrast enhancement is likely so that contrast can be added to a non-contrast-only protocol-and could reduce gadolinium dependence in neuro-oncology imaging. Future studies should optimise these models through close collaboration with expert radiologists.
Funding:
European Society of Radiology; European Institute for Biomedical Imaging Research (EIBIR); Medical Research Council; Wellcome Trust; UCLH NIHR Biomedical Research Centre.