ArticleNeuroradiology2019
Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.
Article in Neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.AJNR. American journal of neuroradiology · 2022Pooled it
- Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs.Diagnostics (Basel, Switzerland) · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.Discover oncology · 2023Article
- Primary central nervous system lymphoma (PCNSL) mimicking a falx meningioma with increasingly massive intracerebral hemorrhage.Radiology case reports · 2023Article
- Article
- A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors.Neuroradiology · 2023Article
- Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling.Current oncology (Toronto, Ont.) · 2022Article
- Brain Tumor Imaging: Applications of Artificial Intelligence.Seminars in ultrasound, CT, and MR · 2022Article
- Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.Cancers · 2022Review
- Use of advanced neuroimaging and artificial intelligence in meningiomas.Brain pathology (Zurich, Switzerland) · 2022Review
- Texture Analysis in Brain Tumor MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Review
- Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification.Frontiers in oncology · 2022Article
- Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study.The neuroradiology journal · 2021Article
- Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.Scientific reports · 2021Article
- Multimodal MRI Assessment of Thalamic Structural Changes in Earthquake Survivors.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWhile MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.
methodsThe study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention.
resultsA binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively.
conclusionA machine learning scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a scheme can be integrated into clinical decision support systems to optimize tumor classification.
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Registered trials
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.