ArticlePloS one2020
High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.PloS one · 2026Article
- Review
- Federated radiomics analysis of preoperative MRI across institutions: toward integrated glioma segmentation and molecular subtyping.Frontiers in radiology · 2025Article
- Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.Frontiers in oncology · 2025Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Diagnosis of choroid plexus papilloma: Current perspectives and future directions.Cancer pathogenesis and therapy · 2024Review
- Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning.AJNR. American journal of neuroradiology · 2024Article
- Comparison of MRI Sequences to PredictBiomedicines · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.Journal of translational medicine · 2023Article
- Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas.NMR in biomedicine · 2023Article
- Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images.Clinics (Sao Paulo, Brazil) · 2023Article
- MRI based radiomics enhances prediction of neurodevelopmental outcome in very preterm neonates.Scientific reports · 2022Article
- Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.AJNR. American journal of neuroradiology · 2022Review
- Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer.Abdominal radiology (New York) · 2022Article
- Evaluation of the dependence of radiomic features on the machine learning model.Insights into imaging · 2022Article
- Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas.Frontiers in neuroscience · 2022Review
- Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer.Frontiers in oncology · 2022Article
- Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics.Insights into imaging · 2021Article
- Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma.Annals of translational medicine · 2021Article
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6 authors.
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Abstract
objectiveTo investigate the performance of high-order radiomics features and models based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) in predicting the immunohistochemical biomarkers of glioma, in order to execute a non-invasive, more precise and personalized glioma disease management.
methods51 pathologically confirmed gliomas patients committed in our hospital from March 2015 to June 2018 were retrospective analysis, and Ki-67, vimentin, S-100 and CD34 immunohistochemical data were collected. The volumes of interest (VOIs) were manually sketched and the radiomics features were extracted. Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). SMOTE technique was used to solve the data bias between two groups. Comprehensive binary logistic regression models were established. Area under the ROC curves (AUC), sensitivity, specificity and accuracy were used to evaluate the predict performance of models. Models reliability were decided according to the standard net benefit of the decision curves.
resultsFour clusters of significant features were screened out and four predicting models were constructed. AUC of Ki-67, S-100, vimentin and CD34 models were 0.713, 0.923, 0.854 and 0.745, respectively. The sensitivities were 0.692, 0.893, 0.875 and 0.556, respectively. The specificities were: 0.667, 0.905, 0.722, and 0.875, with accuracy of 0.660, 0.898, 0.738, and 0.667, respectively. According to the decision curves, the Ki-67, S-100 and vimentin models had reference values.
conclusionThe radiomics features based on T2 FLAIR can potentially predict the Ki-67, S-100, vimentin and CD34 expression. Radiomics model were expected to be a computer-intelligent, non-invasive, accurate and personalized management method for gliomas.
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