ArticleCancer medicine2018
Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma.
Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 5 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 5 syntheses or guidelines pooled it, 106 citations in OpenAlex.
- Application advances of habitat imaging based on CT or PET/CT: a systematic review.La Radiologia medica · 2026Pooled it
- Accuracy of Radiomics in PredictingRadiology. Artificial intelligence · 2024Pooled it
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.European radiology · 2020Pooled it
- Pooled it
- Development and evaluation of multi-strategy fusion machine learning models for predicting postoperative recurrence in non-small cell lung cancer.BMC medical imaging · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Comparison of CT-based radiomics models for the prediction of human epidermal growth factor receptor 2 status in gastric cancer.BMC medical imaging · 2026Article
- IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- A CT-based multi-scale fusion model with SHAP interpretation for preoperative differentiation between lung adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
- Radiomics-based models to predict IDH mutation status and prognosis in gliomas using MRI: a multicenter study.Frontiers in oncology · 2026Article
- Multiparametric MRI radiomics integrating Arterial Spin Labeling, Quantitative Susceptibility Mapping, and Amide Proton Transfer imaging for differentiating glioma recurrence from post-treatment response.Frontiers in oncology · 2026Article
- An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.BMC medical imaging · 2025Article
- Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features.BMC medical imaging · 2025Article
- Bi-phase CT radiomics nomogram for the preoperative prediction of pylorus lymph node metastasis in non-pyloric gastric cancer patients.Abdominal radiology (New York) · 2025Article
- Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma.PloS one · 2025Article
- Prediction of acute pancreatitis severity based on early CT radiomics.BMC medical imaging · 2024Article
- Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.BMC cancer · 2024Article
11 more citing papers are in PubMed but not listed here.
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10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIsocitrate dehydrogenase 1 (IDH1) has been proven as a prognostic and predictive marker in glioblastoma (GBM) patients. The purpose was to preoperatively predict IDH mutation status in GBM using multiregional radiomics features from multiparametric magnetic resonance imaging (MRI).
methodsIn this retrospective multicenter study, 225 patients were included. A total of 1614 multiregional features were extracted from enhancement area, non-enhancement area, necrosis, edema, tumor core, and whole tumor in multiparametric MRI. Three multiregional radiomics models were built from tumor core, whole tumor, and all regions using an all-relevant feature selection and a random forest classification for predicting IDH1. Four single-region models and a model combining all-region features with clinical factors (age, sex, and Karnofsky performance status) were also built. All models were built from a training cohort (118 patients) and tested on an independent validation cohort (107 patients).
resultsAmong the four single-region radiomics models, the edema model achieved the best accuracy of 96% and the best F1-score of 0.75 while the non-enhancement model achieved the best area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort. The overall performance of the tumor-core model (accuracy 0.96, AUC 0.86 and F1-score 0.75) and the whole-tumor model (accuracy 0.96, AUC 0.88 and F1-score 0.75) was slightly better than the single-regional models. The 8-feature all-region radiomics model achieved an improved overall performance of an accuracy 96%, an AUC 0.90, and an F1-score 0.78. Among all models, the model combining all-region imaging features with age achieved the best performance of an accuracy 97%, an AUC 0.96, and an F1-score 0.84.
conclusionsThe radiomics model built with multiregional features from multiparametric MRI has the potential to preoperatively detect the IDH1 mutation status in GBM patients. The multiregional model built with all-region features performed better than the single-region models, while combining age with all-region features achieved the best performance.
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