ArticleScientific reports2019
Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction.AJNR. American journal of neuroradiology · 2023Pooled it
- Noninvasive Determination ofAJNR. American journal of neuroradiology · 2021Pooled it
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- Review
- Article
- Review
- Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Integration of Multi-omics Data Based on Deep Learning for Subtyping of Low-Grade Glioma.Journal of molecular neuroscience : MN · 2025Article
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- [Research Progress in Imaging Investigation of TERT Promoter Mutations in Gliomas].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- -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
- Radical surgical resection with molecular margins is associated with improved survival in IDH wild-type glioblastoma.Neuro-oncology · 2024Article
- Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas.Clinical neuroradiology · 2024Review
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Prediction ofFrontiers in neurology · 2024Article
- Smart brain tumor diagnosis system utilizing deep convolutional neural networks.Multimedia tools and applications · 2023Article
- Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas.Brain tumor pathology · 2023Article
- Impact of GAP-43, Cx43 and actin expression on the outcome and overall survival in diffuse and anaplastic gliomas.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.
Indexed as
Identifiers
What OpenQuestion holds
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.