ArticleNeuro-oncology2025
Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.
Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Proactive concussion prediction using symmetry-aware multiscale explainable hybrid deep learning and multimodal data fusion.Scientific reports · 2026Article
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
- Transformer-Based Deep Learning Model for Predicting Recurrence in High-Grade Glioma.Cancer medicine · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review.Journal of clinical medicine · 2026Review
- Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.Current opinion in pediatrics · 2026Review
- An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.BMC medical imaging · 2026Article
- Image Feature Fusion of Hyperspectral Imaging and MRI for Automated Subtype Classification and Grading of Adult Diffuse Gliomas According to the 2021 WHO Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- FA-DeepMSM: a few-shot adapted interpretable multimodal survival model for improved prognostic prediction in glioblastoma.Scientific reports · 2026Article
- Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.BMC neurology · 2025Article
- Transforming Cancer Care: A Narrative Review on Leveraging Artificial Intelligence to Advance Immunotherapy in Underserved Communities.Journal of clinical medicine · 2025Review
- Review
- Predicting survival in malignant glioma using artificial intelligence.European journal of medical research · 2025Review
- MDL-CA: a multimodal deep learning approach with a cross attention mechanism for accurate brain cancer diagnosis.Frontiers in public health · 2025Article
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model.Frontiers in neurology · 2025Article
- Deep learning-based prediction of TERT mutation status from MRI for glioma molecular subtyping.Frontiers in neurologyArticle
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Authors and funding
23 authors.
Funding
Abstract
backgroundPostoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor features could improve postoperative pLGG risk stratification.
methodsWe used a pretrained DL tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from 2 institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained 3 DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with (1) clinical features, (2) DL-MRI features, and (3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan-Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model.
resultsOf the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, P < .0001).
conclusionsDL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.
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