ArticleClinical neuroradiology2021
Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.
Article in Clinical neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based models and radiomics: can they be reliable predictors for meningioma recurrence? A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Neuroinformatics · 2025Pooled it
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.European radiology · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Establishment and evaluation of an automatic multi?sequence MRI segmentation model of primary central nervous system lymphoma based on the nnU?Net deep learning network method.Oncology letters · 2025Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
- A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.NeuroSci · 2024Article
- A semi-automatic segmentation method for meningioma developed using a variational approach model.The neuroradiology journal · 2024Article
- Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning.World neurosurgery: X · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells.PloS one · 2024Article
- Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.Radiology. Artificial intelligence · 2024Article
- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.Journal of medical Internet research · 2023Article
- Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting.Scientific reports · 2023Article
- Machine learning and deep learning in medicine and neuroimaging.Annals of the Child Neurology Society · 2023Review
- Volumetric measurement of intracranial meningiomas: a comparison between linear, planimetric, and machine learning with multiparametric voxel-based morphometry methods.Journal of neuro-oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeVolumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation.
methodsThe MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus.
resultsOf the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume.
conclusionDeep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.
Indexed as
Identifiers
32060575What OpenQuestion holds
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.