ArticleTranslational oncology2014
Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials.
Article in Translational oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed, 41 citations in OpenAlex.
- The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow.Tomography (Ann Arbor, Mich.) · 2019Trial
- Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery.Molecular imaging and biology · 2016Trial
- Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.Scientific reports · 2016Trial
- Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation.Scientific reports · 2025Article
- Porto Alegre Line predicts lenticulostriate arteries encasement and extent of resection in insular gliomas. A preliminary study.Frontiers in surgery · 2025Article
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.Frontiers in radiology · 2024Article
- Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.Scientific reports · 2023Article
- Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment.Brain informatics · 2023Article
- Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial.Cancers · 2023Article
- Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography.Frontiers in physiology · 2022Article
- Post-operative glioblastoma multiforme segmentation with uncertainty estimation.Frontiers in human neuroscience · 2022Article
- Article
- Review of clinical trials in intraoperative molecular imaging during cancer surgery.Journal of biomedical optics · 2019Review
- Lesion segmentation for MR spectroscopic imaging using the convolution difference method.Magnetic resonance in medicine · 2019Article
- 5-ALA and FDA approval for glioma surgery.Journal of neuro-oncology · 2019Review
- Inter-rater agreement in glioma segmentations on longitudinal MRI.NeuroImage. Clinical · 2019Article
- The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.International journal of radiation oncology, biology, physics · 2018Review
- Rank-Two NMF Clustering for Glioblastoma Characterization.Journal of healthcare engineering · 2018Article
- Rater Reliability of the Hardy Classification for Pituitary Adenomas in the Magnetic Resonance Imaging Era.Journal of neurological surgery. Part B, Skull base · 2017Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T 1-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.
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.