ArticleEuropean radiology2019
Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.
Article in European radiology, 2019. 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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26 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Pooled it
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- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- A bibliometric analysis of research trends and hotspots of pilocytic astrocytoma from 2004 to 2023.Neurosurgical review · 2024Article
- Hypothalamic Hemangioma-like Pilocytic Astrocytoma in an Adult Patient: A Systematic Review with a Focus on Differential Diagnosis and Neurological Presentation.Journal of clinical medicine · 2024Review
- A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.Journal of thoracic disease · 2024Article
- Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography.European radiology · 2023Article
- Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features.Diagnostic pathology · 2023Observational
- The incremental value of CCTA-derived myocardial radiomics signature for ischemia diagnosis with reference to CT myocardial perfusion imaging.The British journal of radiology · 2023Article
- Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- How do phosphodiesterase-5 inhibitors affect cancer? A focus on glioblastoma multiforme.Pharmacological reports : PR · 2022Review
- Brain Tumor Imaging: Applications of Artificial Intelligence.Seminars in ultrasound, CT, and MR · 2022Article
- Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions.The British journal of radiology · 2022Article
- Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm.Computational intelligence and neuroscience · 2022Article
- Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones.Frontiers in oncology · 2021Article
- Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis.Frontiers in oncology · 2021Article
- Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features.Frontiers in medicine · 2021Article
- MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model.
methodsSixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into training and validation data sets (about 2:1). Quantitative radiomic features of the tumours were extracted from contrast-enhanced MR images. A subset of features was selected by feature stability and Boruta algorithm. The selected features were used to build a decision tree model. Predictive accuracy, sensitivity and specificity were used to assess model performance. The classification outcome of the model was combined with tumour location, age and gender features, and multivariable logistic regression analysis and permutation test using the entire data set were performed to further evaluate the decision tree model.
resultsA total of 271 radiomic features were successfully extracted for each tumour. Twelve features were selected as input variables to build the decision tree model. Two features S(1, -1) Entropy and S(2, -2) SumAverg were finally included in the model. The model showed an accuracy, sensitivity and specificity of 0.87, 0.90 and 0.83 for the training data set and 0.86, 0.80 and 0.91 for the validation data set. The classification outcome of the model related to the actual tumour types and did not rely on the other three features (p < 0.001).
conclusionsA decision tree model with two features derived from the contrast-enhanced MR images performed well in differentiating PA from GBM. KEY POINTS: • MRI findings of PA and GBM are sometimes very similar. • Radiomics provides much more quantitative information about tumours. • Radiomic features can help to distinguish PA from GBM.
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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.