ArticleJournal of neuro-oncology2019
Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas.
Article in Journal of neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis 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
71 citing papers in PubMed, 1 synthesis or guideline pooled it, 130 citations in OpenAlex.
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning.Brain and behavior · 2021Trial
- Article
- Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database.European journal of radiology open · 2025Article
- Imaging in Neuro-oncology.Seminars in neurology · 2025Review
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- A three-classification machine learning model for non-invasive prediction of molecular subtypes in diffuse glioma: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- MRI-based machine learning reveals proteasome subunit PSMB8-mediated malignant glioma phenotypes through activating TGFBR1/2-SMAD2/3 axis.Molecular biomedicine · 2025Article
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.Clinical neuroradiology · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Radiology and multi-scale data integration for precision oncology.NPJ precision oncology · 2024Review
- Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.Journal of clinical medicine · 2023Review
- MRI-Based Deep Learning Method for Classification of IDH Mutation Status.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- [Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
11 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
14 authors at 6 institutions in 3 countries.
Funding
Abstract
purposeIsocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.
methodsPreoperative MRIs of 538 glioma patients from three institutions were used as a training cohort. Histogram, shape, and texture features were extracted from preoperative MRIs of T1 contrast enhanced and T2-FLAIR sequences. The extracted features were then integrated with age using a random forest algorithm to generate a model predictive of IDH mutation status and 1p19q codeletion. The model was then validated using MRIs from glioma patients in the Cancer Imaging Archive.
resultsOur model predictive of IDH achieved an area under the receiver operating characteristic curve (AUC) of 0.921 in the training cohort and 0.919 in the validation cohort. Age offered the highest predictive value, followed by shape features. Based on the top 15 features, the AUC was 0.917 and 0.916 for the training and validation cohort, respectively. The overall accuracy for 3 group prediction (IDH-wild type, IDH-mutant and 1p19q co-deletion, IDH-mutant and 1p19q non-codeletion) was 78.2% (155 correctly predicted out of 198).
conclusionUsing machine-learning algorithms, high accuracy was achieved in the prediction of IDH genotype in gliomas and moderate accuracy in a three-group prediction including IDH genotype and 1p19q codeletion.
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.