Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features.

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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

1. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022
   Pooled it
2. A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study.Quantitative imaging in medicine and surgery · 2024
   Article
3. Multi-parametric MRI for radiotherapy simulation.Medical physics · 2023
   Review
4. Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma.Frontiers in oncology · 2021
   Article
5. Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma.Cancers · 2020
   Article
6. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features.Tomography (Ann Arbor, Mich.) · 2020
   Article
7. CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma.Radiology. Imaging cancer · 2020
   Article
8. A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data.Nature machine intelligence · 2020
   Article
9. Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.Neuroradiology · 2020
   Article
10. Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study.Frontiers in oncology · 2020
    Article
11. Precision diagnostics based on machine learning-derived imaging signatures.Magnetic resonance imaging · 2019
    Review
12. Repeatability of Multiparametric Prostate MRI Radiomics Features.Scientific reports · 2019
    Article
13. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.European radiology · 2019
    Article
14. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018
    Review
15. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.NeuroImage. Clinical · 2018
    Article
16. Radiomics in head and neck cancer: from exploration to application.Translational cancer research · 2016
    Article
17. Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.Scientific reports · 2015
    Article
18. Compute Tomography Radiomics Analysis on Whole Pancreas Between Healthy Individual and Pancreatic Ductal Adenocarcinoma Patients: Uncertainty Analysis and Predictive Modeling.Technology in cancer research & treatment
    Article

Corrections and comments

• Erratum for
  Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.2014

Authors and funding

10 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.