The Lancet medical imaging & theranostics
Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study
By Ruffle JK, Mohinta S, Pombo G and 13 others.
Abstract
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.