Trial reportScientific reports2016
Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.
Trial report in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04674579 (The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging), which is not on this map. Cited by 53 papers, 3 of them syntheses that pooled it.
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.
The Added Value of Automatic Segmentation of Cerebral Gliomas in Multi-Sequence Magnetic Resonance Imaging (MRI)
Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled 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
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early,Brain communications · 2026Article
- Imaging in Neuro-oncology.Seminars in neurology · 2025Review
- Structured reporting of gliomas based on VASARI criteria to improve report content and consistency.BMC medical imaging · 2025Article
- Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI.PloS one · 2025Article
- A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs.Frontiers in neuroimaging · 2025Article
- Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.PloS one · 2025Article
- Article
- Locoregional delivery of IL-13Rα2-targeting CAR-T cells in recurrent high-grade glioma: a phase 1 trial.Nature medicine · 2024Article
- Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma.Scientific reports · 2023Article
- Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net.medRxiv : the preprint server for health sciences · 2023Article
- A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.Scientific data · 2023Article
- A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.European radiology experimental · 2023Article
- Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Evaluating automated longitudinal tumor measurements for glioblastoma response assessment.Frontiers in radiology · 2023Article
- The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation.Scientific data · 2022Article
- Article
- PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data.Engineering with computers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.
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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.