ArticleScientific reports2020
A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review.Neurosurgical review · 2026Pooled it
- Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management.Scientific reports · 2025Pooled it
- Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review.Journal of neuroimaging : official journal of the American Society of NeuroimagingPooled it
- Review
- Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.Scientific reports · 2025Article
- Structural-functional fingerprinting for abnormalities investigation in glioma patients.Scientific reports · 2025Article
- Multi-omics insights into Palmitoylation-mediated brain network remodeling and glioblastoma risk.Discover oncology · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Turning attention to tumor-host interface and focus on the peritumoral heterogeneity of glioblastoma.Nature communications · 2024Article
- 1H-MRS parameters in non-enhancing peritumoral regions can predict the recurrence of glioblastoma.Scientific reports · 2024Article
- Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.Radiology. Artificial intelligence · 2024Article
- The peritumoral brain zone in glioblastoma: a review of the pretreatment approach.Polish journal of radiology · 2024Review
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions.Journal of neuro-oncology · 2023Article
- Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.Journal of digital imaging · 2023Article
- Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay.Cancers · 2023Review
- Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.Brain : a journal of neurology · 2023Article
- Article
- The peritumoral brain zone in glioblastoma: where we are and where we are going.Journal of neuroscience research · 2023Review
- Artificial intelligence-based locoregional markers of brain peritumoral microenvironment.Scientific reports · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
The challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.
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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.