ArticleEuropean radiology2019
Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas.
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 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Malignant Progression of Diffuse Low-grade Gliomas: A Systematic Review and Meta-analysis on Incidence and Related Factors.Neurologia medico-chirurgica · 2022Pooled it
- Malignant transformation of lower-grade glioma: contrast enhancement, extent of resection, and the natural history under interval-censored analysis.Journal of neuro-oncology · 2026Article
- Evaluating the predictive efficacy of multi-parameter MRI based radiomic models on clinical symptom progression in perforator artery cerebral infarction.BMC medical imaging · 2026Article
- Brain Magnetic Resonance Imaging Radiomic Signature and Machine Learning Model Prediction of Hepatic Encephalopathy in Adult Cirrhotic Patients.Life (Basel, Switzerland) · 2025Article
- Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas.European radiology · 2023Article
- Determination of prognostic predictors in patients with solitary hepatocellular carcinoma: histogram analysis of multiparametric MRI.Abdominal radiology (New York) · 2023Article
- Characterization and clinical implications of different malignant transformation patterns in diffuse low-grade gliomas.Cancer science · 2023Article
- The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma.Translational cancer research · 2022Article
- Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients.Neuroradiology · 2022Article
- MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke.BMC medical imaging · 2022Article
- Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.World journal of gastroenterology · 2022Article
- Texture Analysis in Brain Tumor MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning.Frontiers in neurology · 2021Article
- Three dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers.Thoracic cancer · 2020Article
- MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma.European radiology · 2020Article
- Texture analysis of deep medullary veins on susceptibility-weighted imaging in infants: evaluating developmental and ischemic changes.European radiology · 2020Article
- Cognitive Functions in Repeated Glioma Surgery.Cancers · 2020Article
- Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.BioMed research international · 2020Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesTexture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.
methodsA total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.
resultsThe ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.
conclusionTexture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning. KEY POINTS: • Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. • Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma. • Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.
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