ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2020
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 2 of them syntheses that pooled it.
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Who cites it
80 citing papers in PubMed, 2 syntheses or guidelines pooled it, 153 citations in OpenAlex.
- Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma.BMC medical imaging · 2023Trial
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- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- Habitat-Derived Radiomic Features of Planning Target Volume to Determine the Local Recurrence After Radiotherapy in Patients with Gliomas: A Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Radiotherapeutic management of brain metastases in patients with breast cancer: a narrative review.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2026Review
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- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Dosiomic and radiomic features within radiotherapy target volume for predicting the treatment response in patients with glioma after radiotherapy.BMC medical imaging · 2025Article
- Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.Scientific reports · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
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- Radiomics-based kidney lesion classification: Mitigating batch effect with nested combat harmonization.Medical physics · 2025Article
- Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.BMC cancer · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMagnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.
methodsThis study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.
resultsFeature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.
conclusionClinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.