ArticleInternational journal of computer assisted radiology and surgery2018
Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- Recovery of cranial nerve neuropathies after LINAC-based stereotactic radiosurgery for benign cavernous sinus meningioma.Journal of neuro-oncology · 2024Article
- Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning.World neurosurgery: X · 2024Article
- Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.Clinical neuroradiology · 2021Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.European radiology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeStereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation.
methodWe present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans.
resultsOur experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations.
conclusionOur algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.
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