ArticleFrontiers in neuroscience2018
Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.Food science & nutrition · 2026Article
- Enhancing clinical insights in glioma grading using Bayesian Optimization and Explainable AI.Journal, genetic engineering & biotechnology · 2026Article
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus.Frontiers in pediatrics · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.Briefings in bioinformatics · 2024Article
- [An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Towards Interpretable, Sequential Multiple Instance Learning: An Application to Clinical Imaging.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.Nature communications · 2023Article
- Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques.Scientific reports · 2023Article
- Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort.The Lancet. Digital health · 2023Article
- A deep learning framework integrating MRI image preprocessing methods for brain tumor segmentation and classification.IBRO neuroscience reports · 2022Article
- Article
- A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.Scientific reports · 2022Article
- Combining Radiology and Pathology for Automatic Glioma Classification.Frontiers in bioengineering and biotechnology · 2022Article
- Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study.Frontiers in oncology · 2022Article
- An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?Journal of geriatric oncology · 2021Article
- Review
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.
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