Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

Huang, Huan; Wang, Feifei; Luo, Shigang; Chen, Guangxiang; Guang-cai, Tang

doi:10.5152/dir.2021.21153

Cited by 7 publications

(8 citation statements)

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“…There were 53 meta-analyses in 38 systematic reviews re-conducted based on extracted or reconstructed data, covering 497 primary studies, 65,955 subjects, and 29,408 events [ 32 , 33 , 35 – 41 , 44 – 47 , 49 – 59 , 61 – 63 , 65 – 75 ] (Fig. 3 ).…”

Section: Resultsmentioning

confidence: 99%

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Zhong

Zhang

et al. 2023

Insights Imaging

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Objective To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. Methods A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. Results We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. Conclusions The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. Clinical relevance statement The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment. Graphical Abstract

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Section: Resultsmentioning

confidence: 99%

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Zhong

Zhang

et al. 2023

Insights Imaging

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“…Our report showed that a patient with high MGMT promoter methylation status has the possibility of tumor shrinkage due to temozolomide and improvement of KPS. Intraoperative diagnosis of MGMT promoter methylation [26] or preoperative assessment by deep machine learning [27] provide useful information in surgical procedures.…”

Section: Discussionmentioning

confidence: 99%

MGMT gene promoter methylation by pyrosequencing method correlates volumetric response and neurological status in IDH wild-type glioblastomas

Hosoya¹,

Takahashi²,

Honda‐Kitahara³

et al. 2022

J Neurooncol

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Purpose Although the usefulness of O6-methylguanine DNA methyltransferase (MGMT) promoter methylation analysis for predicting response to chemoradiotherapy and the prognosis of patients with glioblastoma has been widely reported, there is still no consensus regarding how to define MGMT promoter methylation percentage (MGMTpm%) cutoffs by pyrosequencing method. The aim of this study was to determine the optimal cutoff value of MGMT promoter methylation status using volumetric analysis focused on the tumor volume ratio (TVR) measured by MRI. Methods This retrospective study included newly diagnosed IDH wild-type glioblastoma patients with residual tumor after surgery, followed by local radiotherapy with temozolomide. TVR was defined as the tumor volume at 6 months after the initial chemoradiotherapy administration divided by the tumor volume before the start of therapy. The mean MGMTpm% of 16 CpG islands (74–89) was analyzed using pyrosequencing. We statistically analyzed the correlation between MGMTpm%, TVR, and change in Karnofsky performance status. Results The study included 44 patients with residual tumors. Thirteen (92.9%) of 14 patients with MGMTpm% ≥ 23.9% showed 50% or more volumetric response, leading to prolonged survival, and 17 (70.8%) of 24 patients with MGMTpm% < 8.2% had progressive disease after initial chemoradiotherapy administration. Three (50.0%) of six patients with MGMTpm% 8.2% to < 23.9% had stable disease or partial response. Conclusion Evaluation of MGMTpm% by pyrosequencing is important in predicting the volumetric response and prognosis of glioblastoma patients with residual tumors.

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“…The algorithm achieved an accuracy of $75%. 19 Further research is warranted to fully comprehend the potential of using machine learning to analyze MRI data for predicting MGMT promoter methylation status in brain tumors. However, these preliminary findings are encouraging and suggest that machine learning could serve as a valuable tool for guiding cancer treatment decisions.…”

Section: Related Workmentioning

confidence: 99%

“…The researchers trained a machine learning algorithm to classify tumors as either MGMT‐methylated or MGMT‐unmethylated based on the T1WI data. The algorithm achieved an accuracy of about 75% 19 . More research is needed to fully understand the potential of using machine learning to analyze MRI data for predicting MGMT promoter methylation status in brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

Shim,

Hussain,

Aziz

et al. 2024

Int J Imaging Syst Tech

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Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

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Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

Cited by 7 publications

References 0 publications

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

MGMT gene promoter methylation by pyrosequencing method correlates volumetric response and neurological status in IDH wild-type glioblastomas

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

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