Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning

Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians multimodal reasoning. We explore a two-branch multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal attention strategies. Across nine experimental runs on a balanced 7,200 image dataset, all multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.

Brain tumour MRI typically requires both pre- and post-contrast imaging, but gadolinium is not always desirable (frequent follow-up, renal impairment, allergy, paediatric patients). We developed and validated a deep learning model to predict tumour contrast enhancement from non-contrast MRI alone. We assembled 11,089 brain MRI studies (2006-2024) from 10 datasets across four countries and three continents, spanning adult and paediatric populations with glioma, meningioma, metastases, and post-resection appearances. Three architectures were trained to detect and segment enhancing tumour from T1w, T2w and FLAIR alone. Performance was assessed in a 1,109-study held-out test set (primary endpoint: patient-level enhancement detection; secondary: voxel-level Dice). Eleven expert radiologists attempted the same task on a 564-case subset (100 cases each), blinded to history, prior imaging, and referral. The best model, nnU-Net, achieved 83.0% balanced accuracy (95% CI 79.1-87.2; sensitivity 91.5%, specificity 74.4%) for detection, with R2 = 0.859 for enhancement volume. Of enhancing cases, 76.8% reached Dice >= 0.3, 67.5% >= 0.5, and 50.2% >= 0.7. Under blinded conditions, radiologists' majority vote was lower (71.7% balanced accuracy; sensitivity 77.6%, specificity 65.8%). The proportion reaching Dice >= 0.3 varied by pathology (meningioma 93%, presurgical glioma 76%, metastases 74%, postoperative glioma 74%) and was lowest for paediatric cases (45%). Deep learning can identify contrast-enhancing brain tumours from non-contrast MRI. These models show promise as a triage or decision-support adjunct, such as in flagging studies likely to enhance so that contrast can be added to a non-contrast protocol, and may reduce gadolinium dependence in neuro-oncology imaging. Future work should optimise these models with radiologists.

Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high variability in imaging data and inadequate optimization. Furthermore, radiomic analysis is typically applied only after segmentation is finished, limiting its ability to inform the segmentation process itself. To address these challenges, we propose a novel radiomics-enhanced fused residual multiparametric 3D network (ReFRM3D) for brain tumor characterization. The framework is based on a 3D U-Net architecture and features multi-scale feature fusion, hybrid upsampling, and an extended residual skip mechanism. Additionally, we introduce a radiomic conditioning mechanism that extracts texture and intensity descriptors from an intermediate coarse segmentation and re-injects them into the decoder to refine the final output. Experimental results on BraTS2019, BraTS2020, and BraTS2021 show strong performance, with mean DSC values of 93.45%, 93.61%, and 92.06%, respectively, across whole tumor, enhancing tumor, and tumor core regions. Compared with recent models, ReFRM3D improves average DSC by 5.79%, 7.25%, and 0.96% on these datasets. Our model also generalized well on the BraTS-Africa dataset with an average DSC of 87.8%, despite differences in scanner field strength and patient demographics.