ArticleRadiology. Artificial intelligence2021
Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 36 citations in OpenAlex.
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- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.Diagnostics (Basel, Switzerland) · 2026Article
- Investigation of clinical applicability of deep learning-based virtual contrast-enhanced MRI for nasopharyngeal carcinoma patients.Frontiers in oncology · 2026Article
- Free water elimination tractometry reveals local and remote white matter alterations in diffuse gliomas.Journal of neuro-oncology · 2025Article
- Recommendations on the use of gadolinium-based contrast agents in the diagnosis and monitoring of common adult intracranial tumours.European radiology · 2025Review
- Value of artificial intelligence in neuro-oncology.The Lancet. Digital health · 2025Review
- Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology.Journal of radiation research · 2025Article
- Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset.Clinical neuroradiology · 2025Article
- Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.Medical physics · 2025Article
- Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence.Quantitative imaging in medicine and surgery · 2025Article
- Comparison of intensity normalization methods in prostate, brain, and breast cancer multi-parametric magnetic resonance imaging.Frontiers in oncology · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma. MATERIALS AND
methodsIn this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (
resultsSimulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%;
conclusionThe developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.
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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.