ArticleFrontiers in oncology2021
Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
- Explainable MRI radiomics identifies a PTEN-associated imaging phenotype in adult gliomas.Frontiers in oncology · 2026Article
- Molecular Biomarkers of Glioma.Biomedicines · 2025Review
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.NPJ digital medicine · 2024Article
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.Research square · 2024Article
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
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- Article
- Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.Journal of neuro-oncology · 2023Article
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.BMC cancer · 2022Article
- Article
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Delta-catenin attenuates medulloblastoma cell invasion by targeting EMT pathway.Frontiers in genetics · 2022Article
- Article
- Spherical radiomics for radiogenomic assessment of glioblastoma heterogeneity.Neuro-oncology advancesArticle
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesPhosphatase and tensin homolog (
methodsIn this study, a total of 244 patients with glioma were retrospectively collected from our center (
resultsThe CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91).
conclusionsThe combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of
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