ArticleNeuroImage. Clinical2019
Inter-rater agreement in glioma segmentations on longitudinal MRI.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Patient-reported outcomes as prognostic indicators for overall survival and progression-free survival in brain tumor patients - a systematic review and meta-analysis of randomized clinical trials.Journal of neuro-oncology · 2026Pooled it
- Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma.Journal of neuro-oncology · 2023Trial
- Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.Tomography (Ann Arbor, Mich.) · 2019Trial
- Patient-derived surgical samples reveal the cellular and molecular signatures of glioblastoma infiltration in distinct radiological zones.Brain pathology (Zurich, Switzerland) · 2026Article
- A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.Journal of imaging · 2026Review
- A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points.Scientific reports · 2026Article
- Assessing Impact of Data Quality in Early Post-Operative Glioblastoma Segmentation.Journal of imaging · 2026Article
- Frequency-specific resting state fMRI features in gliomas.Journal of neuro-oncology · 2026Article
- UKAN-EP: enhancing U-KAN with efficient attention and pyramid aggregation for 3D multi-modal MRI brain tumor segmentation.BMC medical imaging · 2025Article
- Study of 18 F-fluciclovine PET for serial assessment of glioblastoma tumor volumes during surgery and radiotherapy.Journal of neuro-oncology · 2025Article
- Collagen disorder architecture features are associated with clinical, molecular, genetic factors and survival outcomes in colon cancer.NPJ precision oncology · 2025Article
- Resection Location and Work Resumption in Patients With Lower-Grade Glioma: A Multicenter Cohort Study.Neurosurgery practice · 2025Article
- The FinnBrain multimodal neonatal template and atlas collection.Communications biology · 2025Article
- GBM-Reservoir: Brain tumor (Glioblastoma Multiforme) MRI dataset collection with ground truth segmentation masks.Data in brief · 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
- Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.Frontiers in oncology · 2025Article
- Patterns of intersectional tumor volumes in T2-weighted MRI and [Scientific reports · 2024Observational
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- 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
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14 authors.
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Abstract
backgroundTumor segmentation of glioma on MRI is a technique to monitor, quantify and report disease progression. Manual MRI segmentation is the gold standard but very labor intensive. At present the quality of this gold standard is not known for different stages of the disease, and prior work has mainly focused on treatment-naive glioblastoma. In this paper we studied the inter-rater agreement of manual MRI segmentation of glioblastoma and WHO grade II-III glioma for novices and experts at three stages of disease. We also studied the impact of inter-observer variation on extent of resection and growth rate.
methodsIn 20 patients with WHO grade IV glioblastoma and 20 patients with WHO grade II-III glioma (defined as non-glioblastoma) both the enhancing and non-enhancing tumor elements were segmented on MRI, using specialized software, by four novices and four experts before surgery, after surgery and at time of tumor progression. We used the generalized conformity index (GCI) and the intra-class correlation coefficient (ICC) of tumor volume as main outcome measures for inter-rater agreement.
resultsFor glioblastoma, segmentations by experts and novices were comparable. The inter-rater agreement of enhancing tumor elements was excellent before surgery (GCI 0.79, ICC 0.99) poor after surgery (GCI 0.32, ICC 0.92), and good at progression (GCI 0.65, ICC 0.91). For non-glioblastoma, the inter-rater agreement was generally higher between experts than between novices. The inter-rater agreement was excellent between experts before surgery (GCI 0.77, ICC 0.92), was reasonable after surgery (GCI 0.48, ICC 0.84), and good at progression (GCI 0.60, ICC 0.80). The inter-rater agreement was good between novices before surgery (GCI 0.66, ICC 0.73), was poor after surgery (GCI 0.33, ICC 0.55), and poor at progression (GCI 0.36, ICC 0.73). Further analysis showed that the lower inter-rater agreement of segmentation on postoperative MRI could only partly be explained by the smaller volumes and fragmentation of residual tumor. The median interquartile range of extent of resection between raters was 8.3% and of growth rate was 0.22 mm/year.
conclusionManual tumor segmentations on MRI have reasonable agreement for use in spatial and volumetric analysis. Agreement in spatial overlap is of concern with segmentation after surgery for glioblastoma and with segmentation of non-glioblastoma by non-experts.
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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.