ArticleScientific reports2023
Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Malignant transformation of lower-grade glioma: contrast enhancement, extent of resection, and the natural history under interval-censored analysis.Journal of neuro-oncology · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- Diagnostic imaging and emerging image-guided neurointervention in brain tumors: a narrative review.Frontiers in radiology · 2026Review
- Brain Tumors, AI and Psychiatry: Predicting Tumor-Associated Psychiatric Syndromes with Machine Learning and Biomarkers.International journal of molecular sciences · 2025Review
- Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.BMC medical imaging · 2025Article
- Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.Neurosurgical review · 2025Article
- Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.Neuro-oncology · 2025Article
- Low-grade gliomas do not grow along white matter tracts: evidence from quantitative imaging.Brain communications · 2025Article
- Study on the classification of benign and malignant breast lesions using a multi-sequence breast MRI fusion radiomics and deep learning model.European journal of radiology open · 2024Article
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25 authors.
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Abstract
Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.
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