ArticleClinics (Sao Paulo, Brazil)2023
Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images.
Article in Clinics (Sao Paulo, Brazil), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Progression of the Residual Lesion in Cavernous Sinus Extra-Axial Cavernous Hemangioma After Surgery.Translational stroke research · 2025Article
- Development and validation of a combined radiomic and clinical model based on contrast-enhanced ultrasound for preoperative prediction of CK19-positive hepatocellular carcinoma.Abdominal radiology (New York) · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. DATA AND
methodsThe authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness.
resultsAge was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values.
conclusionA nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.
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