ArticleEuropean radiology2022
A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Predicting recurrence in silent corticotroph adenomas: a habitat analysis and comprehensive nomogram approach.Neuroradiology · 2026Article
- Review
- A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.PloS one · 2026Article
- Explainable incremental-value analysis of apparent diffusion coefficient and arterial spin labeling radiomics for ATRX status prediction in glioblastoma.Frontiers in oncology · 2026Article
- Radiomics based on preoperative magnetic resonance imaging predict the cell lineages of nonfunctioning pituitary neuroendocrine tumors.Neuroradiology · 2025Article
- Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study.Discover oncology · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas.Clinical neuroradiology · 2024Review
- A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study.Quantitative imaging in medicine and surgery · 2024Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Article
- Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas.European radiology · 2023Article
- Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.Cancers · 2023Article
- The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery.Frontiers in oncology · 2023Article
- Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images.Clinics (Sao Paulo, Brazil) · 2023Article
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- Multi-b-value diffusion stretched-exponential model parameters correlate with MIB-1 and CD34 expression in Glioma patients, an intraoperative MR-navigated, biopsy-based histopathologic study.Frontiers in oncology · 2023Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III).
methodsA total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T
resultsThe radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status.
conclusionsThe nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients. KEY POINTS: • Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.
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