ArticleJournal of digital imaging2007
Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.
Article in Journal of digital imaging, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- The TRUTH confirmed: validation of an intraindividual comparison of gadobutrol and gadoteridol for imaging of glioblastoma using quantitative enhancement analysis.European radiology experimental · 2021Article
- Understanding artificial intelligence based radiology studies: What is overfitting?Clinical imaging · 2020Review
- Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.Medical & biological engineering & computing · 2018Article
- Artificial intelligence in radiology.Nature reviews. Cancer · 2018Review
- Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.International journal of biomedical imaging · 2015Article
- Change detection of medical images using dictionary learning techniques and principal component analysis.Journal of medical imaging (Bellingham, Wash.) · 2014Article
- DEWEY: the DICOM-enabled workflow engine system.Journal of digital imaging · 2014Article
- Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.Medical physics · 2014Article
- Article
- Optimal presentation modes for detecting brain tumor progression.AJNR. American journal of neuroradiology · 2011Article
- The optimal linear transformation-based fMRI feature space analysis.Medical & biological engineering & computing · 2009Article
- Change detection & characterization: a new tool for imaging informatics and cancer research.Cancer informatics · 2007Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.
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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.