Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

Xi, Yi‐Bin; Guo, Fan; Xu, Zhibo; Li, Chen; Wei, Wei; Tian, Ping; Liu, Ting‐ting; Liu, Lin; Chen, Gang; Ye, Jing; Cheng, Guang; Cui, Long‐Biao; Zhang, Hong‐juan; Qin, Wei; Yin, Hong

doi:10.1002/jmri.25860

Cited by 115 publications

(99 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In previous work, Xi et al showed that a radiomics signature derived from T1-WI, T2-WI and enhanced T1-WI was a potential imaging marker for the prediction of MGMT promoter methylation in GBMs, with prediction accuracy of 86.59% in the training cohort and 80% in the validation cohort [30]. However, MGMT methylated patients not only behaved well in GBM, but also presented with prolonged survival in lower-grade astrocytomas [4,31].…”

Section: Discussionmentioning

confidence: 99%

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication

et al. 2018

View full text Add to dashboard Cite

Objectives Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. Methods We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. Results The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into highrisk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). Conclusions The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. Key Points • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas.• Habitat-based radiomics can improve efficacy in predicting MGMT methylation status.• Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect.

show abstract

Section: Discussionmentioning

confidence: 99%

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Radiomics is a research branch in the field of medical imaging (10). Based on the rapid development of machine learning and image processing techniques, radiomic analyses have been successfully applied in the field of oncology (11)(12)(13)(14)(15)(16), including glioma (17). Magnetic resonance imaging (MRI) is a routinely used diagnostic tool for glioma management.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose: The majority of patients with low-grade gliomas (LGGs) experience tumor-related epilepsy during the disease course. Our study aimed to build a radiomic prediction model for LGG-related epilepsy type based on magnetic resonance imaging (MRI) data.Methods: A total of 205 cases with LGG-related epilepsy were enrolled in the retrospective study and divided into training and validation cohorts (1:1) according to their surgery time. Seven hundred thirty-four radiomic features were extracted from T2-weighted imaging, including six location features. Pearson correlation coefficient, univariate area under curve (AUC) analysis, and least absolute shrinkage and selection operator regression were adopted to select the most relevant features for the epilepsy type to build a radiomic signature. Furthermore, a novel radiomic nomogram was developed for clinical application using the radiomic signature and clinical variables from all patients.Results: Four MRI-based features were selected from the 734 radiomic features, including one location feature. Good discriminative performances were achieved in both training (AUC = 0.859, 95% CI = 0.787-0.932) and validation cohorts (AUC = 0.839, 95% CI = 0.761-0.917) for the type of epilepsy. The accuracies were 80.4 and 80.6%, respectively. The radiomic nomogram also allowed for a high degree of discrimination. All models presented favorable calibration curves and decision curve analyses. Conclusion:Our results suggested that the MRI-based radiomic analysis may predict the type of LGG-related epilepsy to enable individualized therapy for patients with LGG-related epilepsy.

show abstract

“…The aim of radiomics is to evaluate the potential relationship between image and biological features of solid tumors [1] in order to access prognostic information before treatment and optimize therapeutic strategies. This approach has been recently applied to various tumors, such as glioblastoma [2][3][4], clear-cell renal carcinoma [5,6], breast, rectal and lung adenocarcinoma [7][8][9][10][11]. Recent studies also demonstrated how quantitative texture analysis (QTA) may be used to complement conventional imaging features [12][13][14][15][16][17]; however, further investigations are needed to deepen the knowledge on the relationship between radiological and biological phenotypes and to enlarge the role of radiomics in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging

et al. 2018

View full text Add to dashboard Cite

show abstract

Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

Abstract: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1380-1387.

Cited by 115 publications

References 39 publications

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging

Contact Info

Product

Resources

About