ArticleActa neurochirurgica. Supplement2022
Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.
Article in Acta neurochirurgica. Supplement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Spatial Mapping of Glioblastoma Infiltration: Diffusion Tensor Imaging-Based Radiomics and Connectomics in Recurrence Prediction.Brain sciences · 2025Review
- Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics.Journal of neuro-oncology · 2025Article
- Computational Fractal-Based Neurosurgery.Advances in experimental medicine and biology · 2024Review
- Artificial Intelligence in Brain Tumors.Advances in experimental medicine and biology · 2024Review
- Fractals in Neuroimaging.Advances in neurobiology · 2024Article
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Fractal Geometry Meets Computational Intelligence: Future Perspectives.Advances in neurobiology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.
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Registered trials
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