ArticleFrontiers in oncology2020
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 39 citations in OpenAlex.
- Conventional MRI-Based Semantic Features for Differentiation of Pediatric Medulloblastoma and Ependymoma in the Fourth Ventricle: Insights From a Multi-Center Retrospective Study.CNS neuroscience & therapeutics · 2026Article
- Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations.Cancers · 2026Article
- Explainable multi-modal machine learning for predicting occult pulmonary metastases in differentiated thyroid cancer: a SHAP-based approach prior to radioactive iodine scans.Frontiers in medical technology · 2025Article
- GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.Physics in medicine and biology · 2024Article
- Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.Cancer medicine · 2024Article
- Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings.Journal of imaging · 2024Review
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- Machine learning for classification of cutaneous sebaceous neoplasms: implementing decision tree model using cytological and architectural features.Diagnostic pathology · 2023Observational
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data.Frontiers in oncology · 2023Article
- Machine Learning in the Classification of Pediatric Posterior Fossa Tumors: A Systematic Review.Cancers · 2022Review
- Prediction of lung metastases in thyroid cancer using machine learning based on SEER database.Cancer medicine · 2022Article
- Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.AJNR. American journal of neuroradiology · 2022Review
- Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.Cancers · 2022Article
- Article
- Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.Cancers · 2022Review
- ADC Histogram Analysis of Pediatric Low-Grade Glioma Treated with Selumetinib: A Report from the Pediatric Brain Tumor Consortium.AJNR. American journal of neuroradiology · 2022Article
- Review
- Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.Frontiers in public health · 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
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (
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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.