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.

翻译：脑肿瘤影像学评估通常需要对比剂注射前后的磁共振成像，但钆剂注射并非总是可行，例如在频繁随访、肾功能不全、过敏或儿科患者中。本研究旨在开发并验证一种能够仅从非对比增强MRI序列预测脑肿瘤对比强化的深度学习模型。我们整合了来自10个国际数据集的11089例脑MRI研究，涵盖成人和儿童群体，包含多种神经肿瘤状态（如胶质瘤、脑膜瘤、转移瘤及术后表现）。训练深度学习模型（nnU-Net、SegResNet、SwinUNETR）仅使用非对比增强T1加权、T2加权及T2/FLAIR加权图像来预测并分割强化肿瘤。在1109例预留测试患者上使用患者级检测指标和体素级分割精度评估模型性能。将模型预测结果与11位专家放射科医师（每位医师随机审阅100例患者）的评估进行对比。性能最佳的nnU-Net模型在检测强化肿瘤时达到83%的平衡准确率、91.5%的敏感性和74.4%的特异性。强化体积预测与真实值高度相关（R² 0.859）。该模型表现优于专家放射科医师（平均准确率69.8%、敏感性75.9%、特异性64.7%）。76.8%的测试患者Dice系数超过0.3（可接受检测），67.5%超过0.5（良好检测），50.2%超过0.7（优秀检测）。深度学习能够以临床相关性能从非对比增强MRI中识别对比强化脑肿瘤。这些模型有望成为筛查工具，并可能降低神经肿瘤影像学对钆剂的依赖。未来研究应与放射学专家共同评估其临床效用。