ArticleCancers2021
Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Artificial Intelligence in Schizophrenia Spectrum Disorders: Current Use and Future Perspectives. A Systematic Mapping Literature Review.Alpha psychiatry · 2026Pooled it
- Predicting 1p/19q Codeletion Status in Glioma Using MRI-Derived Radiomics: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.AJNR. American journal of neuroradiology · 2025Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- The importance of spatial resolution in detecting chronic myocardial iron remnants following hemorrhagic myocardial infarction with T2* MRI.Biomedical physics & engineering express · 2026Article
- Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics.Cancer medicine · 2026Article
- Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP.Biomedical physics & engineering express · 2026Article
- Predicting Prostate Cancer Risk and Its Associated Factors Using Machine Learning Techniques: A Retrospective Study.Health science reports · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- A Language Performance Model for Predicting Glioma Recurrence and Molecular Biomarkers: A Retrospective Cohort Study.Brain and behavior · 2026Article
- Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction.Computers in biology and medicine · 2026Article
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- PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer.Military Medical Research · 2026Article
- Novel compounds of Taiwanese green propolis induce apoptosis of human glioblastoma cells by daylight photodynamic action.Future science OA · 2025Article
- Genetic Artificial Intelligence in Gastrointestinal Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Neural Processing of Noise-Vocoded Speech Under Divided Attention: An fMRI-Machine Learning Study.Human brain mapping · 2025Article
- Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.BMC cancer · 2025Article
- A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids.iScience · 2025Article
- Article
- Revealing multiple biological subtypes of schizophrenia through a data-driven approach.Journal of translational medicine · 2025Article
- Exosomes in glioma: mechanistic insights on biological, therapeutic, and diagnostic perspective.Therapeutic delivery · 2025Review
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.
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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.