ArticleScientific reports2021
Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Pilot research on predicting the sub-volume with high risk of tumor recurrence inside peritumoral edema using the ratio-maxiADC/meanADC from the advanced MRI.Discover oncology · 2025Article
- Artificial Intelligence-Based MRI Segmentation for the Differential Diagnosis of Single Brain Metastasis and Glioblastoma.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis.Journal of magnetic resonance imaging : JMRI · 2025Article
- Modulation and distribution of extracellular free water and tract deficits in rhesus macaques before and after the initiation of emtricitabine + tenofovir disoproxil fumarate + dotutegravir treatment.Frontiers in immunology · 2025Article
- Research progress in artificial intelligence for brain metastases.Frontiers in medicine · 2025Review
- Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.Discover 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
- Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.Cancers · 2023Review
- Functional biomaterials for biomimetic 3D in vitro tumor microenvironment modeling.In vitro models · 2023Review
- Artificial intelligence-based locoregional markers of brain peritumoral microenvironment.Scientific reports · 2023Article
- Preoperative validation of edema-corrected tractography in neurosurgical practice: translating surgeon insights into novel software implementation.Frontiers in neurology · 2023Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.Combinatorial chemistry & high throughput screening · 2023Article
- Construction of VGG16 Convolution Neural Network (VGG16_CNN) Classifier with NestNet-Based Segmentation Paradigm for Brain Metastasis Classification.Sensors (Basel, Switzerland) · 2022Article
- Inherited genetics of adult diffuse glioma and polygenic risk scores-a review.Neuro-oncology practice · 2022Review
- Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.Cancers · 2022Review
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6 authors.
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Abstract
Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.
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