ArticleThe neuroradiology journal2015
Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.
Article in The neuroradiology journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics.Neuroradiology · 2016Pooled it
- Exosomal bioactive molecules: molecular regulation, functional heterogeneity, and translational implications in disease diagnostics and therapeutics.Clinical and experimental medicine · 2026Review
- Integrating single-cell transcriptomics with deep learning for glioblastoma treatment.Frontiers in genetics · 2026Review
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.International journal of molecular sciences · 2025Review
- Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.Current computer-aided drug design · 2025Article
- Review
- IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI.Diagnostics (Basel, Switzerland) · 2024Article
- An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.Radiology. Imaging cancer · 2024Review
- The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.Lasers in medical science · 2022Article
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Review
- Differentiation of multiple sclerosis lesions and low-grade brain tumors on MRS data: machine learning approaches.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
- Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?Medical oncology (Northwood, London, England) · 2021Review
- Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.Radiology. Imaging cancer · 2020Review
- Multivariable non-invasive association of isocitrate dehydrogenase mutational status in World Health Organization grade II and III gliomas with advanced magnetic resonance imaging T2 mapping techniques.The neuroradiology journal · 2020Article
- Changes in metabolites in the brain of patients with fibromyalgia after treatment with an NMDA receptor antagonist.The neuroradiology journal · 2019Article
- Machine Learning Versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database.World neurosurgery: X · 2019Article
- Review
- Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine.Frontiers in oncology · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextWith the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification.
aimsThe aim of the study is to classify gliomas into benign and malignant types using MRI data. SETTINGS AND
designRetrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. METHODS AND MATERIALS: Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning.
resultsThree of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%).
conclusionsThe performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences.
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