ArticleNMR in biomedicine2022
Synthetic MRI improves radiomics-based glioblastoma survival prediction.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.European radiology · 2026Article
- Multiparametric magnetic resonance imaging (MRI) integrating amide proton transfer-weighted (APTw) imaging, synthetic MRI, and multiplexed sensitivity encoding diffusion-weighted imaging (MUSE DWI) in the diagnosis and grading of prostate cancer.Quantitative imaging in medicine and surgery · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.Sensors (Basel, Switzerland) · 2024Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Predictive value of MRI features on glioblastoma.European radiology · 2023Article
- Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI.Frontiers in neuroimaging · 2023Review
- Synthetic MRI improves radiomics-based glioblastoma survival prediction.NMR in biomedicine · 2022Article
- Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and
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