ArticleEuropean radiology2017
Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05739500 (The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software), which is not on this map. Cited by 106 papers, 6 of them syntheses that pooled it.
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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.
The trial behind it
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.
The Clinical Trial 01 to Evaluate the Effectiveness of MRI-based Computer Aided Diagnosis Software (V1) for Glioma Segmentation, Gene Prediction and Tumor Grading
Who cites it
106 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Noninvasive Determination ofAJNR. American journal of neuroradiology · 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
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis.European radiology · 2019Pooled it
- Integrating MRI radiomics and transcriptomics to predict IDH mutation status and prognosis in glioma.Cancer cell international · 2026Article
- Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology.Frontiers in oncology · 2026Review
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma.iScience · 2025Article
- Predictive value of models based on MRI radiomics and clinical indicators for lymphovascular space invasion in endometrial cancer.BMC cancer · 2025Article
- Auxiliary diagnosis of primary bone tumors based on Machine learning model.Journal of bone oncology · 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
- A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Two-Stage Training Framework Using Multicontrast MRI Radiomics forRadiology. Artificial intelligence · 2024Article
- Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.European radiology · 2024Article
- A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.Journal of cancer research and clinical oncology · 2024Article
- Deep Learning: A Primer for Neurosurgeons.Advances in experimental medicine and biology · 2024Review
- OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.International journal of computer assisted radiology and surgery · 2023Article
- Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review.Cancers · 2023Review
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe status of isocitrate dehydrogenase 1 (IDH1) is highly correlated with the development, treatment and prognosis of glioma. We explored a noninvasive method to reveal IDH1 status by using a quantitative radiomics approach for grade II glioma.
methodsA primary cohort consisting of 110 patients pathologically diagnosed with grade II glioma was retrospectively studied. The radiomics method developed in this paper includes image segmentation, high-throughput feature extraction, radiomics sequencing, feature selection and classification. Using the leave-one-out cross-validation (LOOCV) method, the classification result was compared with the real IDH1 situation from Sanger sequencing. Another independent validation cohort containing 30 patients was utilised to further test the method.
resultsA total of 671 high-throughput features were extracted and quantized. 110 features were selected by improved genetic algorithm. In LOOCV, the noninvasive IDH1 status estimation based on the proposed approach presented an estimation accuracy of 0.80, sensitivity of 0.83 and specificity of 0.74. Area under the receiver operating characteristic curve reached 0.86. Further validation on the independent cohort of 30 patients produced similar results.
conclusionsRadiomics is a potentially useful approach for estimating IDH1 mutation status noninvasively using conventional T2-FLAIR MRI images. The estimation accuracy could potentially be improved by using multiple imaging modalities. KEY POINTS: • Noninvasive IDH1 status estimation can be obtained with a radiomics approach. • Automatic and quantitative processes were established for noninvasive biomarker estimation. • High-throughput MRI features are highly correlated to IDH1 states. • Area under the ROC curve of the proposed estimation method reached 0.86.
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Registered trials
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.