ArticleAcademic radiology2012
Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation.
Article in Academic radiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Quantitative MRI in Neuroimaging: A Review of Techniques, Biomarkers, and Emerging Clinical Applications.Brain sciences · 2025Review
- AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.Journal of imaging informatics in medicine · 2025Article
- A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs.Frontiers in neuroimaging · 2025Article
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.Scientific reports · 2023Article
- Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice.European radiology · 2018Article
- Article
- Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.Cancer imaging : the official publication of the International Cancer Imaging Society · 2015Article
- Iterative probabilistic voxel labeling: automated segmentation for analysis of The Cancer Imaging Archive glioblastoma images.AJNR. American journal of neuroradiology · 2015Article
- Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma.Journal of neuro-oncology · 2015Article
- Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.PloS one · 2015Article
- Computer-extracted MR imaging features are associated with survival in glioblastoma patients.Journal of neuro-oncology · 2014Article
- Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.AJNR. American journal of neuroradiology · 2014Article
- Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials.Translational oncology · 2014Article
- Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.Korean journal of radiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
RATIONALE AND
objectivesQuantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. MATERIALS AND
methodsOur software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input.
resultsTwenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface.
conclusionValidation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.
Indexed as
Identifiers
What 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.