ArticleCancers2020
Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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.
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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
28 citing papers in PubMed.
- Application of Enhanced T1WI of MRI Radiomics in Glioma Grading.International journal of clinical practice · 2022Trial
- Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.Journal of translational medicine · 2026Article
- 3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.Journal of neuropathology and experimental neurology · 2025Article
- Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification.IEEE access : practical innovations, open solutions · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Article
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
- Increased MRI-based Brain Age in chronic migraine patients.The journal of headache and pain · 2023Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- Dual Deep CNN for Tumor Brain Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Digital Pathology and Artificial Intelligence Applications in Pathology.Brain tumor research and treatment · 2022Review
- P2X7 receptor: the regulator of glioma tumor development and survival.Purinergic signalling · 2022Article
- The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.Frontiers in oncology · 2022Article
- Prognostic Biomarker KIF18A and Its Correlations With Immune Infiltrates and Mitosis in Glioma.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (
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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.