ArticleAJNR. American journal of neuroradiology2014
Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme.
Article in AJNR. American journal of neuroradiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas.AJNR. American journal of neuroradiology · 2015Pooled it
- Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab-a report from the BELOB trial.Neuro-oncology · 2017Trial
- Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery.Molecular imaging and biology · 2016Trial
- Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [Scientific reports · 2024Article
- Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.Cancers · 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
- Prognostic value of MRI volumetric parameters in non-small cell lung cancer patients after immune checkpoint inhibitor therapy: comparison with response assessment criteria.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.Scientific reports · 2022Article
- Conventional and advanced imaging throughout the cycle of care of gliomas.Neurosurgical review · 2021Review
- Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging.Journal of clinical medicine · 2021Article
- Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.Korean journal of radiology · 2020Review
- Validation of an effective implantable pump-infusion system for chronic convection-enhanced delivery of intracerebral topotecan in a large animal model.Journal of neurosurgery · 2020Article
- Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- Accuracy of the neurosurgeons estimation of extent of resection in glioblastoma.Acta neurochirurgica · 2020Article
- Automated quantitative assessment of oncological disease progression using deep learning.Annals of translational medicine · 2019Article
- Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging.Cancers · 2019Review
- Monitoring optic chiasmatic-hypothalamic glioma volumetric changes by MRI in children under clinical surveillance or chemotherapy.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2019Article
- Monitoring Radiation Treatment Effects in Glioblastoma: FLAIR Volume as Significant Predictor of Survival.Tomography (Ann Arbor, Mich.) · 2017Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
background and purposeA limitation in postoperative monitoring of patients with glioblastoma is the lack of objective measures to quantify residual and recurrent disease. Automated computer-assisted volumetric analysis of contrast-enhancing tissue represents a potential tool to aid the radiologist in following these patients. In this study, we hypothesize that computer-assisted volumetry will show increased precision and speed over conventional 1D and 2D techniques in assessing residual and/or recurrent tumor. MATERIALS AND
methodsThis retrospective study included patients with native glioblastomas with MR imaging performed at 24-48 hours following resection and 2-4 months postoperatively. 1D and 2D measurements were performed by 2 neuroradiologists with Certificates of Added Qualification. Volumetry was performed by using manual segmentation and computer-assisted volumetry, which combines region-based active contours and a level set approach. Tumor response was assessed by using established 1D, 2D, and volumetric standards. Manual and computer-assisted volumetry segmentation times were compared. Interobserver correlation was determined among 1D, 2D, and volumetric techniques.
resultsTwenty-nine patients were analyzed. Discrepancy in disease status between 1D and 2D compared with computer-assisted volumetry was 10.3% (3/29) and 17.2% (5/29), respectively. The mean time for segmentation between manual and computer-assisted volumetry techniques was 9.7 minutes and <1 minute, respectively (P < .01). Interobserver correlation was highest for volumetric measurements (0.995; 95% CI, 0.990-0.997) compared with 1D (0.826; 95% CI, 0.695-0.904) and 2D (0.905; 95% CI, 0.828-0.948) measurements.
conclusionsComputer-assisted volumetry provides a reproducible and faster volumetric assessment of enhancing tumor burden, which has implications for monitoring disease progression and quantification of tumor burden in treatment trials.
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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.