ArticleMedical & biological engineering & computing2023
A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.
Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Insights into pet-based radiogenomics in oncology: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants.Neurosurgical review · 2025Pooled it
- Pathway dysregulation and therapeutic resistance in glioblastoma: molecular mechanisms and emerging therapeutic targets.Future science OA · 2026Review
- Overexpression of EZH2 is associated with clinicopathological parameters and poor prognosis in gliomas.Oncology letters · 2026Article
- The importance of spatial resolution in detecting chronic myocardial iron remnants following hemorrhagic myocardial infarction with T2* MRI.Biomedical physics & engineering express · 2026Article
- Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP.Biomedical physics & engineering express · 2026Article
- ¹⁹F MRI radiomic features: in vitro and in vivo repeatability.European radiology experimental · 2026Article
- A Language Performance Model for Predicting Glioma Recurrence and Molecular Biomarkers: A Retrospective Cohort Study.Brain and behavior · 2026Article
- Enhanced CT-based deep learning radiomics and biological correlations for predicting immunotherapy efficacy in advanced non-small cell lung cancer.Translational cancer research · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
- Spatial associations between esophageal lesions and surrounding tissues in esophageal fistula: a CAM-guided radiomics study.Frontiers in oncology · 2026Article
- Novel compounds of Taiwanese green propolis induce apoptosis of human glioblastoma cells by daylight photodynamic action.Future science OA · 2025Article
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
- Survival risk stratification of 2021 WHO glioblastoma by MRI radiomics and biological exploration.BMC cancer · 2025Article
- PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Predictive value of temporal muscle thickness for prognosis in newly diagnosed IDH wild-type glioblastoma patients: evaluated for a Chinese population.European radiology · 2025Article
- A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids.iScience · 2025Article
- Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.Psychiatry and clinical neurosciences · 2025Article
- Revealing multiple biological subtypes of schizophrenia through a data-driven approach.Journal of translational medicine · 2025Article
- Exosomes in glioma: mechanistic insights on biological, therapeutic, and diagnostic perspective.Therapeutic delivery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Lower-grade gliomas (LGG) can eventually progress to glioblastoma (GBM) and death. In the context of the transfer learning approach, we aimed to train and test an MRI-based radiomics model for predicting survival in GBM patients and validate it in LGG patients. From each patient's 704 MRI-based radiomics features, we chose seventeen optimal radiomics signatures in the GBM training set (n = 71) and used these features in both the GBM testing set (n = 31) and LGG validation set (n = 107) for further analysis. Each patient's risk score, calculated based on those optimal radiomics signatures, was chosen to represent the radiomics model. We compared the radiomics model with clinical, gene status models, and combined model integrating radiomics, clinical, and gene status in predicting survival. The average iAUCs of combined models in training, testing, and validation sets were respectively 0.804, 0.878, and 0.802, and those of radiomics models were 0.798, 0.867, and 0.717. The average iAUCs of gene status and clinical models ranged from 0.522 to 0.735 in all three sets. The radiomics model trained in GBM patients can effectively predict the overall survival of GBM and LGG patients, and the combined model improved this ability.
Indexed as
Identifiers
37432527What OpenQuestion holds
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.