ArticleMedicine2019
Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis.
Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and validation of a convolutional neural network for automatic differentiation of primary central nervous system lymphoma and glioblastoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Bi-exponential diffusion-weighted imaging for differentiating high-grade gliomas from solitary brain metastases: a VOI-based histogram analysis.Scientific reports · 2024Article
- Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes.Discover oncology · 2023Article
- Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data.Cancers · 2022Article
- Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis.La Radiologia medica · 2022Article
- Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.Cancers · 2022Review
- A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study.Frontiers in oncology · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 to June 2018, 24 patients with GBM and 18 patients with sBM were included in this retrospective study. The enhancing area was first segmented on the post-contrast T1WI, then the segmentation was copied to the absolute CBV map and histogram analysis was finally performed. Unpaired t test was used to select the features that could separate two entities and receiving operating curve was used to test the diagnostic performance. Finally, a machine learning method was used to test the diagnostic performance combing all the selected features.Six of 19 features were feasible to distinguish GBM from sBM (all P < .001), among which energy had the highest diagnostic performance (area under curve, 0.84; accuracy, 88%), while a machine learning method could improve the diagnostic performance (area under curve, 0.94; accuracy, 95%).Histogram analysis of the absolute CBV in the enhancing area could help us distinguish GBM from sBM, in addition, a machine learning method with combined features is preferable. It is quite helpful in the condition that the biological nature of peritumoral edema could not separate these two entities.
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Registered trials
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