ArticleActa neurochirurgica2020
Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.
Article in Acta neurochirurgica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Combining radiomics of X-rays with patient functional rating scales for predicting satisfaction after radial fracture fixation: a multimodal machine learning predictive model.BMC musculoskeletal disorders · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Nomogram prediction of molecular characteristics in WHO grade 3-4 diffuse gliomas based on fractal analysis and VASARI features.Scientific reports · 2025Article
- Enhancement of the nontumor component in newly diagnosed glioblastoma as a more accurate predictor of local recurrence location: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning.World neurosurgery: X · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Assessment of Ki-67 expression levels in IDH-wildtype glioblastoma using logistic regression modelling of VASARI features.Neurosurgical review · 2023Article
- Interpreting deep learning models for glioma survival classification using visualization and textual explanations.BMC medical informatics and decision making · 2023Article
- Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).JCO clinical cancer informatics · 2023Article
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Effects of Multi-Shell Free Water Correction on Glioma Characterization.Diagnostics (Basel, Switzerland) · 2021Article
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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3 authors.
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Abstract
backgroundMeasurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.
methodsPreoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival.
resultsMedian Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82-0.98) compared to 0.91 (IQR, 0.83-0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63-0.95) compared to 0.81 (IQR, 0.69-0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74-0.98) compared to 0.83 (IQR, 0.78-0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47-0.97) compared to 0.67 (IQR, 0.42-0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67-13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06-32.12], corrected p = 0.011) were independently associated with overall survival.
conclusionSemi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance.
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