ArticleJournal of neuro-oncology2017
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.
Article in Journal of neuro-oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.International journal of molecular sciences · 2019Pooled it
- Roadmap to Local Tumour Growth: Insights from Cervical Cancer.Scientific reports · 2019Trial
- Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP.Biomedical physics & engineering express · 2026Article
- IDH mutation prediction in non-enhancing gliomas with relaxed T2-FLAIR mismatch and fractal dimension: a two-center study.BMC medical imaging · 2026Article
- Attention-enhanced SAM with PBFO tuning: advancing glioma MRI segmentation.Frontiers in medicine · 2026Article
- Improving radiomics-based isocitrate dehydrogenase 1 prediction in glioma patients using semi-supervised machine learning models.BMC medical imaging · 2025Article
- DSIT UNet a dual stream iterative transformer based UNet architecture for segmenting brain tumors from FLAIR MRI images.Scientific reports · 2025Article
- Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation.Journal of imaging · 2025Article
- Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification.International journal of biomedical imaging · 2025Article
- MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.Scientific reports · 2024Article
- Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.IBRO neuroscience reports · 2024Article
- The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics.Cancers · 2024Review
- Advantages of transformer and its application for medical image segmentation: a survey.Biomedical engineering online · 2024Review
- An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor.Scientific reports · 2024Article
- Advanced image generation for cancer using diffusion models.Biology methods & protocols · 2024Article
- Tumor radiogenomics in gliomas with Bayesian layered variable selection.Medical image analysis · 2023Article
- Machine learning and deep learning for brain tumor MRI image segmentation.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Article
- Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images.Diagnostics (Basel, Switzerland) · 2023Article
- Reviewing Federated Machine Learning and Its Use in Diseases Prediction.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient outcomes using algorithmic measurements of tumor shape in magnetic resonance imaging (MRI). We analyzed preoperative imaging and genomic subtype data from 110 patients with lower-grade gliomas (WHO grade II and III) from The Cancer Genome Atlas. Computer algorithms were applied to analyze the imaging data and provided five quantitative measurements of tumor shape in two and three dimensions. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. Patient outcomes were quantified by overall survival. We found that there is a strong association between angular standard deviation (ASD), which measures irregularity of the tumor boundary, and the IDH-1p/19q subtype (p < 0.0017), RNASeq cluster (p < 0.0002), DNA copy number cluster (p < 0.001), and the cluster of clusters (p < 0.0002). The RNASeq cluster was also associated with bounding ellipsoid volume ratio (p < 0.0005). Tumors in the IDH wild type cluster and R2 RNASeq cluster which are associated with much poorer outcomes generally had higher ASD reflecting more irregular shape. ASD also showed association with patient overall survival (p = 0.006). Shape features in MRI were strongly associated with genomic subtypes and patient outcomes in lower-grade glioma.
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