ArticleJournal of neuro-oncology2017
Intra-rater variability in low-grade glioma segmentation.
Article in Journal of neuro-oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 63 citations in OpenAlex.
- Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment ofAJNR. American journal of neuroradiology · 2024Trial
- Multidisciplinary recommendations for routine follow-up of isocitrate dehydrogenase‑mutant glioma in 2026: Adapting clinical practice to the molecular era.Neuro-oncology practice · 2026Review
- A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points.Scientific reports · 2026Article
- Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas.Communications medicine · 2025Article
- The use of minor response, volumetric assessment, and growth rate kinetics as endpoints in grade 1-3 glioma clinical trials: a RANO perspective.Neuro-oncology · 2025Review
- Structured reporting of gliomas based on VASARI criteria to improve report content and consistency.BMC medical imaging · 2025Article
- Exploring the Impact of Variability in Cell Segmentation and Tracking Approaches.Microscopy research and technique · 2025Article
- RapidBrachyIVBT: A dosimetry software for patient-specific intravascular brachytherapy dose calculations on optical coherence tomography images.Medical physics · 2025Article
- Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Applications of machine learning to MR imaging of pediatric low-grade gliomas.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Review
- Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.Scientific reports · 2024Article
- Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.International journal of computer assisted radiology and surgery · 2024Article
- Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.Journal of imaging informatics in medicine · 2024Article
- Treatment evaluation by volumetric segmentation in pediatric optic pathway glioma: evaluation of the effect of bevacizumab on intra-tumor components.Journal of neuro-oncology · 2024Article
- Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network.European radiology · 2023Article
- Monitored anesthesia care and asleep-awake-asleep techniques combined with multiple monitoring for resection of gliomas in eloquent brain areas: a retrospective analysis of 225 patients.Chinese neurosurgical journal · 2022Article
- Article
- A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas.Sensors (Basel, Switzerland) · 2022Article
- Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI.Clinics and practice · 2022Article
- Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors.BMC biomedical engineering · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of size and growth are key radiological factors in low-grade gliomas (LGGs), both for prognostication and treatment evaluation, but the reliability of LGG-segmentation is scarcely studied. With a diffuse and invasive growth pattern, usually without contrast enhancement, these tumors can be difficult to delineate. The aim of this study was to investigate the intra-observer variability in LGG-segmentation for a radiologist without prior segmentation experience. Pre-operative 3D FLAIR images of 23 LGGs were segmented three times in the software 3D Slicer. Tumor volumes were calculated, together with the absolute and relative difference between the segmentations. To quantify the intra-rater variability, we used the Jaccard coefficient comparing both two (J2) and three (J3) segmentations as well as the Hausdorff Distance (HD). The variability measured with J2 improved significantly between the two last segmentations compared to the two first, going from 0.87 to 0.90 (p = 0.04). Between the last two segmentations, larger tumors showed a tendency towards smaller relative volume difference (p = 0.07), while tumors with well-defined borders had significantly less variability measured with both J2 (p = 0.04) and HD (p < 0.01). We found no significant relationship between variability and histological sub-types or Apparent Diffusion Coefficients (ADC). We found that the intra-rater variability can be considerable in serial LGG-segmentation, but the variability seems to decrease with experience and higher grade of border conspicuity. Our findings highlight that some criteria defining tumor borders and progression in 3D volumetric segmentation is needed, if moving from 2D to 3D assessment of size and growth of LGGs.
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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.