Identifying the Association of Contrast Enhancement with Vascular Endothelia Growth Factor Expression in Anaplastic Gliomas: A Volumetric Magnetic Resonance Imaging Analysis

Wang, Yinyan; Wang, Kai; Li, Hongming; Wang, Jiangfei; Wang, Lei; Dai, Jianping; Jiang, Tao; Ma, Jun

doi:10.1371/journal.pone.0121380

Cited by 8 publications

(5 citation statements)

References 37 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9, 33,73 Areas of non-enhancing tumour in GBM imply decreased vascular permeability corresponded with low VEGF levels. 60, 74 Upregulated VEGF is also associated with malignancy and microvascular density 75 although no direct approach to quantifiable parameters found.…”

Section: Resultsmentioning

confidence: 99%

Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Seow

Wong

Ahmad‐Annuar

et al. 2018

The British Journal of Radiology

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Seow

Wong

Ahmad‐Annuar

et al. 2018

The British Journal of Radiology

View full text Add to dashboard Cite

show abstract

“…It has been reported that VEGF status was associated with some imaging features [29, 34–38]. A previous study reported that gliomas of different VEGF status tended to be situated at different locations [39].…”

Section: Discussionmentioning

confidence: 99%

“…VEGF expression level was scored according to clinical practice: (−) represented no or rare expression (< 5% positive cells); (+) represented mild expression (6–25% positive cells); (++) represented moderate expression (26–50% positive cells); and (+++) represented strong expression (> 50% positive cells). Low VEGF expression was defined as VEGF (− and +), and high VEGF expression was defined as VEGF (++ and +++) [29].…”

Section: Methodsmentioning

confidence: 99%

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

Sun

Wang

et al. 2019

Cancer Imaging

Self Cite

View full text Add to dashboard Cite

ObjectiveTo predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis.Materials and methodsPreoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II–IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed.ResultsNine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups.ConclusionsRadiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.

show abstract

“…The best method to determine the VEGF expression status of glioma by radiological features has been explored using different magnetic resonance imaging (MRI) modalities. On routine MRI, the volumetric ratio of enhancement to necrosis was significantly higher in patients with low VEGF expression than in those with high VEGF expression 8 ; VEGF expression was also positively correlated with peripheral edema index, enhancement percentage, and the maximum diameter of glioma 9 . On perfusion-weighted imaging, cerebral blood volume maps were correlated with VEGF expression in nonenhanced gliomas 10 .…”

mentioning

confidence: 90%

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma

Zheng,

Chen,

Zhang

et al. 2023

J Comput Assist Tomogr

View full text Add to dashboard Cite

Objectives This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM). Methods We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression. The machine learning models were established with the maximum relevance and minimum redundancy algorithm and the extreme gradient boosting classifier. The area under the receiver operating curve (AUC) and accuracy were calculated for the training and validation sets. Results Positive VEGF in GBM was 63.1% (137/217), with a high expression ratio of 53.3% (73/137). To predict the positive and negative VEGF expression, 7 radiomic features were selected, with 3 features from T1CE and 4 from T2WI. The accuracy and AUC were 0.83 and 0.81, respectively, in the training set and were 0.73 and 0.74, respectively, in the validation set. To predict high and low levels, 7 radiomic features were selected, with 2 from T1CE, 1 from T2WI, and 4 from the data combinations of T1CE and T2WI. The accuracy and AUC were 0.88 and 0.88, respectively, in the training set and were 0.72 and 0.72, respectively, in the validation set. Conclusion The VEGF expression status in GBM can be predicted using a machine learning model. Radiomic features resulting from data combinations of different MRI sequences could be helpful.

show abstract

Identifying the Association of Contrast Enhancement with Vascular Endothelia Growth Factor Expression in Anaplastic Gliomas: A Volumetric Magnetic Resonance Imaging Analysis

Cited by 8 publications

References 37 publications

Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma

Contact Info

Product

Resources

About