Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

Han, Yu; Yan, Lin‐Feng; Wang, Xibin; Sun, Ying-Zhi; Zhang, Xin; Liu, Zhicheng; Nan, Hai-Yan; Hu, Yu-Chuan; Yang, Yang; Zhang, Jin; Yu, Ying; Sun, Qian; Tian, Qiang; Hu, Bo; Xiao, Gang; Wang, Wen; Cui, Guangbin

doi:10.1186/s12885-018-4114-2

Cited by 82 publications

(83 citation statements)

References 32 publications

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“…The study data of the current project were derived from a diagnostic trial that has been registered to ClinicalTrials.gov with the trial protocol published and the study protocol was approved. The study population is the same as the one described in a previous work and written informed consent was obtained from all individuals . A total of 117 histologically confirmed (WHO 2007) glioma patients were retrospectively enrolled from July 2014 to January 2016, involving 25 WHO grade II, 29 WHO grade III, and 63 WHO grade IV.…”

Section: Methodsmentioning

confidence: 99%

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Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Yang

Yan

Zhang

et al. 2019

Magnetic Resonance Imaging

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Background Accurate glioma grading plays an important role in patient treatment. Purpose To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). Study Type Retrospective. Population In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. Field Strength/Sequence 3.0T MRI/ T1WI, T2 fluid‐attenuated inversion recovery, contrast enhanced T1, arterial spinal labeling, diffusion‐weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2), and dynamic contrast‐enhanced. Assessment Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF‐SVM) combined with attribute selection using SVM‐recursive feature elimination (SVM‐RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10‐fold crossvalidation. The model performance was further tested using the remaining 20% data. Statistical Tests Ten‐fold crossvalidation was used to validate the model performance. Results Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM‐RFE was able to reduce attribute redundancy as well as improve RBF‐SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray‐level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM‐RFE to provide comparable classifying efficacy. Data Conclusion When using image textures‐based SVM classification of gliomas, the GLSZM model in combination with gray‐level 64 and attribute selection may be an optimized solution. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263–1274.

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Section: Methodsmentioning

confidence: 99%

“…The study population is the same as the one described in a previous work 23 and written informed consent was obtained from all individuals. 24…”

Section: Patientsmentioning

confidence: 99%

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Yang

Yan

Zhang

et al. 2019

Magnetic Resonance Imaging

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“…40,42 MR may also not predict molecular characteristics of primary brain tumours, such as MGMT methylation and IDH mutation status. Han et al 43 correlated the MGMT promoter methylation with tumour location and necrosis by using advanced MRI. However, Moon et al 44 did not find significant association between MGMT methylation status and tumour location.…”

Section: Current Approaches To the Management Of Gbm Diagnosismentioning

confidence: 99%

Circulating biomarkers in patients with glioblastoma

et al. 2019

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Gliomas are the most common tumours of the central nervous system and the most aggressive form is glioblastoma (GBM). Despite advances in treatment, patient survival remains low. GBM diagnosis typically relies on imaging techniques and postoperative pathological diagnosis; however, both procedures have their inherent limitations. Imaging modalities cannot differentiate tumour progression from treatment-related changes that mimic progression, known as pseudoprogression, which might lead to misinterpretation of therapy response and delay clinical interventions. In addition to imaging limitations, tissue biopsies are invasive and most of the time cannot be performed over the course of treatment to evaluate 'real-time' tumour dynamics. In an attempt to address these limitations, liquid biopsies have been proposed in the field. Blood sampling is a minimally invasive procedure for a patient to endure and could provide tumoural information to guide therapy. Tumours shed tumoural content, such as circulating tumour cells, cell-free nucleic acids, proteins and extracellular vesicles, into the circulation, and these biomarkers are reported to cross the blood-brain barrier. The use of liquid biopsies is emerging in the field of GBM. In this review, we aim to summarise the current literature on circulating biomarkers, namely circulating tumour cells, circulating tumour DNA and extracellular vesicles as potential non-invasively sampled biomarkers to manage the treatment of patients with GBM.

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“…In a model combining tumor location, necrosis, ADC and rCBF, the authors were able to establish MGMT promoter methylation status with an impressive area under the curve (AUC) of 0.91. 90 …”

Section: Clinical Applications Of Advanced Mrimentioning

confidence: 99%

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

Pope¹,

Brandal²

2018

Q J Nucl Med Mol Imaging

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Magnetic resonance imaging is integral to the care of patients with high-grade gliomas. Anatomic detail can be acquired with conventional structural imaging, but newer approaches also add capabilities to interrogate image-derived physiologic and molecular characteristics of central nervous system neoplasms. These advanced imaging techniques are increasingly employed to generate biomarkers that better reflect tumor burden and therapy response. The following is an overview of current strategies based on advanced magnetic resonance imaging that are used in the assessment of high-grade glioma patients with an emphasis on how novel imaging biomarkers can potentially advance patient care.

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Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

Cited by 82 publications

References 32 publications

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Circulating biomarkers in patients with glioblastoma

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

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