ArticleJournal of digital imaging2017
Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
85 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
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.BMC cancer · 2025Article
- OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 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
- Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European radiology · 2024Article
- FAM109B plays a tumorigenic role in low-grade gliomas and is associated with tumor-associated macrophages (TAMs).Journal of translational medicine · 2024Article
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Deep learning and transfer learning for brain tumor detection and classification.Biology methods & protocols · 2024Article
- Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets.Insights into imaging · 2023Article
- Brain tumor image segmentation based on improved FPN.BMC medical imaging · 2023Article
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- Artificial intelligence in neuro-oncology.Frontiers in neuroscience · 2023Review
25 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Several studies have linked codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression-free survival. Hence, predicting 1p/19q status is crucial for effective treatment planning of LGG. In this study, we predict the 1p/19q status from MR images using convolutional neural networks (CNN), which could be a non-invasive alternative to surgical biopsy and histopathological analysis. Our method consists of three main steps: image registration, tumor segmentation, and classification of 1p/19q status using CNN. We included a total of 159 LGG with 3 image slices each who had biopsy-proven 1p/19q status (57 non-deleted and 102 codeleted) and preoperative postcontrast-T1 (T1C) and T2 images. We divided our data into training, validation, and test sets. The training data was balanced for equal class probability and was then augmented with iterations of random translational shift, rotation, and horizontal and vertical flips to increase the size of the training set. We shuffled and augmented the training data to counter overfitting in each epoch. Finally, we evaluated several configurations of a multi-scale CNN architecture until training and validation accuracies became consistent. The results of the best performing configuration on the unseen test set were 93.3% (sensitivity), 82.22% (specificity), and 87.7% (accuracy). Multi-scale CNN with their self-learning capability provides promising results for predicting 1p/19q status non-invasively based on T1C and T2 images. Predicting 1p/19q status non-invasively from MR images would allow selecting effective treatment strategies for LGG patients without the need for surgical biopsy.
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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.