ArticleQuantitative imaging in medicine and surgery2022
Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies.Neuroradiology · 2025Pooled it
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.European radiology · 2026Article
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.NPJ digital medicine · 2026Article
- Isocitrate Dehydrogenase-Mutant Astrocytomas: Risk Stratification and Therapeutic Advance.MedComm · 2026Review
- Predicting IDH Mutation in Glioma Patients Using Deep Learning Algorithms with Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Incorporation of Edited MRS into Clinical Practice May Improve Care of Patients withAJNR. American journal of neuroradiology · 2025Article
- Diagnostic accuracy of artificial intelligence based on imaging data for predicting distant metastasis of colorectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Article
- Using artificial intelligence based imaging to predict lymph node metastasis in non-small cell lung cancer: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2024Article
- Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European radiology · 2024Article
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.The neuroradiology journal · 2024Article
- Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement.Neuroradiology · 2023Article
- 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
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis.Cancers · 2023Review
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4 authors.
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Abstract
Background: Conventionally, identifying isocitrate dehydrogenase ( Methods: A systematic search of Cochrane Library, Web of Science, Medline, and Scopus was conducted to identify relevant publications until August 1, 2021. Articles were included if all the following criteria were met: (I) patients with histopathologically confirmed World Health Organization (WHO) grade II, III, or IV gliomas; (II) histopathological examination with the Results: Four studies with a total of 1,295 patients were included. In the training set, the pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were 93.9%, 90.9% and 0.958, respectively. In the validation set, the pooled sensitivity, specificity, and area under the SROC curve were 90.8%, 85.5% and 0.939, respectively. With a known pretest probability of 80.2%, the Bayes theorem yielded a posttest probability of 97.6% and 96.0% for a positive test and 27.0% and 30.6% for a negative test for training sets and validation sets, respectively. Discussion: This is the first meta-analysis that summarizes the diagnostic performance of DL in predicting
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