ArticleFrontiers in oncology2020
Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05062772 (Predicting Overall Survival in Glioblastomas Using Radiomic Features of Intraoperative Ultrasound. A Proposal for the Creation of an International Database of Brain Tumor Ultrasound Images), which is not on this map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Predicting Overall Survival in Glioblastomas Using Radiomic Features of Intraoperative Ultrasound. A Proposal for the Creation of an International Database of Brain Tumor Ultrasound Images
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Hyperspectral imaging for tumor resection guidance in surgery: a systematic review of preclinical and clinical studies.Journal of biomedical optics · 2025Pooled it
- Applications of elastography in operative neurosurgery: A systematic review.Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia · 2022Pooled it
- Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.International journal of molecular sciences · 2025Review
- Ultrasound elastic modulus reconstruction using a deep learning model trained with simulated data.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Advancements in Imaging and Neurosurgical Techniques for Brain Tumor Resection: A Comprehensive Review.Cureus · 2024Review
- Navigated intraoperative ultrasound in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- Article
- [A multi-modal feature fusion classification model based on distance matching and discriminative representation learning for differentiation of high-grade glioma from solitary brain metastasis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Machine Learning and Radiomics in Gliomas.Advances in experimental medicine and biology · 2024Review
- Review
- Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.Frontiers in oncology · 2023Review
- Cranial sonolucent prosthesis: a window of opportunity for neuro-oncology (and neuro-surgery).Journal of neuro-oncology · 2022Article
- Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review.Frontiers in surgery · 2022Review
- Multiparametric Intraoperative Ultrasound in Oncological Neurosurgery: A Pictorial Essay.Frontiers in neuroscience · 2022Review
- Intraoperative Ultrasound Shear-Wave Elastography in Focal Cortical Dysplasia Surgery.Journal of clinical medicine · 2021Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe differential diagnosis of glioblastomas (GBM) from solitary brain metastases (SBM) is essential because the surgical strategy varies according to the histopathological diagnosis. Intraoperative ultrasound elastography (IOUS-E) is a relatively novel technique implemented in the surgical management of brain tumors that provides additional information about the elasticity of tissues. This study compares the discriminative capacity of intraoperative ultrasound B-mode and strain elastography to differentiate GBM from SBM.
methodsWe performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with glioblastoma (GBM) and solitary brain metastases (SBM) diagnoses. Cases with an intraoperative ultrasound study were included. Images were acquired before dural opening, first in B-mode, and then using the strain elastography module. After image pre-processing, an analysis based on deep learning was conducted using the open-source software Orange. We have trained an existing neural network to classify tumors into GBM and SBM
resultsA total of 36 patients were included in the analysis, 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images, 435 of B-mode, 265 (60.92%) corresponded to GBM and 170 (39.8%) to metastases. In addition, 377 elastograms, 232 (61.54%) GBM and 145 (38.46%) metastases were analyzed. For B-mode, AUC and accuracy values of the classification algorithms ranged from 0.790 to 0.943 and from 72 to 89%, respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79% to 95%, respectively.
conclusionAutomated processing of ultrasound images through deep learning can generate high-precision classification algorithms that differentiate glioblastomas from metastases using intraoperative ultrasound. The best performance regarding AUC was achieved by the elastography-based model supporting the additional diagnostic value that this technique provides.
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