ArticleJournal of medical imaging (Bellingham, Wash.)2020
Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities.
Abstract read
In one paragraph
Article in Journal of medical imaging (Bellingham, Wash.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
–field-weighted citation impact
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction.AJNR. American journal of neuroradiology · 2023Pooled it
- Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.Medical physics · 2020Pooled it
- Impact on survival of glioblastoma patients in relation to the imaging of the peri-surgical area: a multi-parametric diffusion MRI, perfusion MRI, and [11C]MET PET study.The British journal of radiology · 2026Article
- A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma.Neuro-oncology · 2026Article
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Can diffusion-based generated magnetic resonance images predict glioma methylation accurately?Quantitative imaging in medicine and surgery · 2025Article
- Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.Neuro-oncology · 2025Article
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning-based overall survival prediction in patients with glioblastoma: An automatic end-to-end workflow using pre-resection basic structural multiparametric MRIs.Computers in biology and medicine · 2025Article
- Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.Health science reports · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma.Heliyon · 2024Article
- Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.Frontiers in neuroscience · 2024Article
- A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.Medical & biological engineering & computing · 2023Article
- Article
- Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.International journal of imaging systems and technology · 2023Article
- Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Spyridon BakasUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-8734-6482
Gaurav ShuklaUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Hamed AkbariUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Guray ErusUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-6633-4861
Aristeidis SotirasUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-0795-8820
Saima RathoreUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Chiharu SakoUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-3243-3954
Sung Min HaUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Martin RozyckiUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Russell T ShinoharaUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-8627-8203
Michel BilelloUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Christos DavatzikosUniversity of Pennsylvania, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, Richards Medical Research Laboratories, Philadelphia, PA, United States.
Funding
Statistical atlases of brain tumor MRI:do imaging phenotypes predict progression?R01NS042645 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2002 to 2024
$8.7MCancer imaging phenomics software suite: application to brain and breast cancerU24CA189523 · NCI · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS, KONTOS, DESPINA · 2015 to 2019
$3.2MBiomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9MNCI NIH HHS U24 CA189523NIH HHS S10 OD023495NINDS NIH HHS R01 NS042645
Abstract
PubMed holds no abstract for this paper.
Indexed as
glioblastomamultivariatepredictionprognosisradiomicssurvival
Identifiers
What OpenQuestion holds
Textmetadata
Read underepoch 390
Registered trials
None linked
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.