Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics

Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis

OBJECTIVES

To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas.

METHODS

PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity.

RESULTS

We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01).

CONCLUSIONS

Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice.

CLINICAL RELEVANCE STATEMENT

MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery.

KEY POINTS

• Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models

We aimed to study whether the Bruton's tyrosine kinase (BTK) expression is correlated with the prognosis of patients with high-grade gliomas (HGGs) and predict its expression level prior to surgery, by constructing radiomic models. Clinical and gene expression data of 310 patients from The Cancer Genome Atlas (TCGA) were included for gene-based prognostic analysis. Among them, contrast-enhanced T1-weighted imaging (T1WI + C) from The Cancer Imaging Archive (TCIA) with genomic data was selected from 82 patients for radiomic models, including support vector machine (SVM) and logistic regression (LR) models. Furthermore, the nomogram incorporating radiomic signatures was constructed to evaluate its clinical efficacy. BTK was identified as an independent risk factor for HGGs through univariate and multivariate Cox regression analyses. Three radiomic features were selected to construct the SVM and LR models, and the validation set showed area under curve (AUCs) values of 0.711 (95% CI, 0.598-0.824) and 0.736 (95% CI, 0.627-0.844), respectively. The median survival times of the high Rad_score and low-Rad_score groups based on LR model were 15.53 and 23.03 months, respectively. In addition, the total risk score of each patient was used to construct a predictive nomogram, and the AUCs calculated from the corresponding time-dependent ROC curves were 0.533, 0.659, and 0.767 for 1, 3, and 5 years, respectively. BTK is an independent risk factor associated with poor prognosis in patients, and the radiomic model constructed in this study can effectively and non-invasively predict preoperative BTK expression levels and patient prognosis based on T1WI + C.

Quantitative and Qualitative Parameters of DCE-MRI Predict CDKN2A/B Homozygous Deletion in Gliomas

RATIONALE AND OBJECTIVES

Homozygous deletion (HD) of CDKN2A/B holds important prognostic value in gliomas. This study aimed to explore the predictive potential of conventional MRI characteristics combined with dynamic contrast-enhanced MRI parameters in predicting CDKN2A/B HD status in gliomas.

MATERIALS AND METHODS

Preoperative MRI data of 105 patients (69 without CDKN2A/B HD, and 36 with CDKN2A/B homozygous deletion) with gliomas were retrospectively collected. Conventional MRI features and dynamic contrast-enhanced-MRI qualitative parameter time-intensity curve type, quantitative parameters Ktrans, Kep, Ve, Vp, and iAUC were obtained. Logistic regression models for prediction of CDKN2A/B HD status were constructed in all types of gliomas and both subtypes of IDH-mutant and IDH-wild gliomas.

RESULTS

Multivariate analysis for all patients demonstrated that age (OR=1.103, p = 0.002) and Ktrans (OR=1.051, p < 0.001) independently predicted CDKN2A/B HD. In IDH-mutant subgroup, multivariate analysis results indicated that Ktrans (OR=1.098, p = 0.031) emerged as autonomous predictors of CDKN2A/B HD. In IDH-wild subgroup, age (OR=1.111, p = 0.002) and Ktrans (OR=1.032, p = 0.001) were independent predictors of CDKN2A/B HD according to the multivariate analysis. The areas under the receiver operating characteristic curve of the corresponding models were 0.90, 0.95 and 0.84, respectively.

CONCLUSION

Ktrans can serve as valuable predictive parameters for identifying CDKN2A/B HD status in all types of gliomas and both subtypes of IDH-mutant and IDH-wild gliomas. These findings provide a foundation for precise preoperative non-invasive diagnosis and personalized treatment approaches for glioma patients.

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI

BACKGROUND

Cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion has been verified as an independent and critical biomarker of negative prognosis and short survival in isocitrate dehydrogenase (IDH)-mutant astrocytoma. Therefore, noninvasive and accurate discrimination of CDKN2A/B homozygous deletion status is essential for the clinical management of IDH-mutant astrocytoma patients.

PURPOSE

To develop a noninvasive, robust preoperative model based on MR image features for discriminating CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma.

STUDY TYPE

Retrospective.

POPULATION

Two hundred fifty-one patients: 107 patients with CDKN2A/B homozygous deletion and 144 patients without CDKN2A/B homozygous deletion.

FIELD STRENGTH/SEQUENCE

3.0 T/1.5 T: Contrast-enhanced T1-weighted spin-echo inversion recovery sequence (CE-T1WI) and T2-weighted fluid-attenuation spin-echo inversion recovery sequence (T2FLAIR).

ASSESSMENT

A total of 1106 radiomics and 1000 deep learning features extracted from CE-T1WI and T2FLAIR were used to develop models to discriminate the CDKN2A/B homozygous deletion status. Radiomics models, deep learning-based radiomics (DLR) models and the final integrated model combining radiomics features with deep learning features were developed and compared their preoperative discrimination performance.

STATISTICAL TESTING

Pearson chi-square test and Mann Whitney U test were used for assessing the statistical differences in patients' clinical characteristics. The Delong test compared the statistical differences of receiver operating characteristic (ROC) curves and area under the curve (AUC) of different models. The significance threshold is P < 0.05.

RESULTS

The final combined model (training AUC = 0.966; validation AUC = 0.935; test group: AUC = 0.943) outperformed the optimal models based on only radiomics or DLR features (training: AUC = 0.916 and 0.952; validation: AUC = 0.886 and 0.912; test group: AUC = 0.862 and 0.902).

DATA CONCLUSION

Whether based on a single sequence or a combination of two sequences, radiomics and DLR models have achieved promising performance in assessing CDKN2A/B homozygous deletion status. However, the final model combining both deep learning and radiomics features from CE-T1WI and T2FLAIR outperformed the optimal radiomics or DLR model.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Post-treatment imaging of gliomas: challenging the existing dogmas

Gliomas are the commonest malignant central nervous system tumours in adults and imaging is the cornerstone of diagnosis, treatment, and post-treatment follow-up of these patients. With the ever-evolving treatment strategies post-treatment imaging and interpretation in glioma remains challenging, more so with the advent of anti-angiogenic drugs and immunotherapy, which can significantly alter the appearance in this setting, thus making interpretation of routine imaging findings such as contrast enhancement, oedema, and mass effect difficult to interpret. This review details the various methods of management of glioma including the upcoming novel therapies and their impact on imaging findings, with a comprehensive description of the imaging findings in conventional and advanced imaging techniques. A systematic appraisal for the existing and emerging techniques of imaging in these settings and their clinical application including various response assessment guidelines and artificial intelligence based response assessment will also be discussed.

Computational pathology-based weakly supervised prediction model for MGMT promoter methylation status in glioblastoma

Introduction

The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) is closely related to the treatment and prognosis of glioblastoma. However, there are currently some challenges in detecting the methylation status of MGMT promoters. The hematoxylin and eosin (H&E)-stained histopathological slides have always been the gold standard for tumor diagnosis.

Methods

In this study, based on the TCGA database and H&E-stained Whole slide images (WSI) of Beijing Tiantan Hospital, we constructed a weakly supervised prediction model of MGMT promoter methylation status in glioblastoma by using two Transformer structure models.

Results

The accuracy scores of this model in the TCGA dataset and our independent dataset were 0.79 (AUC = 0.86) and 0.76 (AUC = 0.83), respectively.

Conclusion

The model demonstrates effective prediction of MGMT promoter methylation status in glioblastoma and exhibits some degree of generalization capability. At the same time, our study also shows that adding Patches automatic screening module to the computational pathology research framework of glioma can significantly improve the model effect.

A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase-mutant high-grade astrocytoma: a multicenter study

Background

The mutational status of alpha-thalassemia X-linked intellectual disability (ATRX) is an important indicator for the treatment and prognosis of high-grade gliomas, but reliable ATRX testing currently requires invasive procedures. The objective of this study was to develop a clinical trait-imaging fusion model that combines preoperative magnetic resonance imaging (MRI) radiomics and deep learning (DL) features with clinical variables to predict ATRX status in isocitrate dehydrogenase (IDH)-mutant high-grade astrocytoma.

Methods

A total of 234 patients with IDH-mutant high-grade astrocytoma (120 ATRX mutant type, 114 ATRX wild type) from 3 centers were retrospectively analyzed. Radiomics and DL features from different regions (edema, tumor, and the overall lesion) were extracted to construct multiple imaging models by combining different features in different regions for predicting ATRX status. An optimal imaging model was then selected, and its features and linear coefficients were used to calculate an imaging score. Finally, a fusion model was developed by combining the imaging score and clinical variables. The performance and application value of the fusion model were evaluated through the comparison of receiver operating characteristic curves, the construction of a nomogram, calibration curves, decision curves, and clinical application curves.

Results

The overall hybrid model constructed with radiomics and DL features from the overall lesion was identified as the optimal imaging model. The fusion model showed the best prediction performance with an area under curve of 0.969 in the training set, 0.956 in the validation set, and 0.949 in the test set as compared to the optimal imaging model (0.966, 0.916, and 0.936, respectively) and clinical model (0.677, 0.641, 0.772, respectively).

Conclusions

The clinical trait-imaging fusion model based on preoperative MRI could effectively predict the ATRX mutation status of individuals with IDH-mutant high-grade astrocytoma and has the potential to help patients through the development of a more effective treatment strategy before treatment.

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma

OBJECTIVES

To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)-mutant astrocytoma.

METHODS

Multiparametric brain MRI data and corresponding genomic information of 234 subjects (111 positives for CDKN2A/B homozygous deletion and 123 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. Two independent multi-sequence networks (ResFN-Net and FN-Net) are built on the basis of ResNet and ConvNeXt network combined with attention mechanism to classify CDKN2A/B homozygous deletion status using MR images including contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). The performance of the network is summarized by three-way cross-validation; ROC analysis is also performed.

RESULTS

The average cross-validation accuracy (ACC) of ResFN-Net is 0.813. The average cross-validation area under curve (AUC) of ResFN-Net is 0.8804. The average cross-validation ACC and AUC of FN-Net is 0.9236 and 0.9704, respectively. Comparing all sequence combinations of the two networks (ResFN-Net and FN-Net), the sequence combination of CE-T1WI and T2WI performed the best, and the ACC and AUC were 0.8244, 0.8975 and 0.8971, 0.9574, respectively.

CONCLUSIONS

The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma.

CLINICAL RELEVANCE STATEMENT

A novel deep learning network (FN-Net) based on preoperative MRI was developed to predict the CDKN2A/B homozygous deletion status. This network has the potential to be a practical tool for the noninvasive characterization of CDKN2A/B in glioma to support personalized classification and treatment planning.

KEY POINTS

• CDKN2A/B homozygous deletion status is an important marker for glioma grading and prognosis. • An MRI-based deep learning approach was developed to predict CDKN2A/B homozygous deletion status. • The predictive performance based on ConvNeXt network was better than that of ResNet network.

Development of A Radiomic Model for MGMT Promoter Methylation Detection in Glioblastoma Using Conventional MRI

The methylation of the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a molecular marker associated with a better response to chemotherapy in patients with glioblastoma (GB). Standard pre-operative magnetic resonance imaging (MRI) analysis is not adequate to detect MGMT promoter methylation. This study aims to evaluate whether the radiomic features extracted from multiple tumor subregions using multiparametric MRI can predict MGMT promoter methylation status in GB patients. This retrospective single-institution study included a cohort of 277 GB patients whose 3D post-contrast T1-weighted images and 3D fluid-attenuated inversion recovery (FLAIR) images were acquired using two MRI scanners. Three separate regions of interest (ROIs) showing tumor enhancement, necrosis, and FLAIR hyperintensities were manually segmented for each patient. Two machine learning algorithms (support vector machine (SVM) and random forest) were built for MGMT promoter methylation prediction from a training cohort (196 patients) and tested on a separate validation cohort (81 patients), based on a set of automatically selected radiomic features, with and without demographic variables (i.e., patients' age and sex). In the training set, SVM based on the selected radiomic features of the three separate ROIs achieved the best performances, with an average of 83.0% (standard deviation: 5.7%) for accuracy and 0.894 (0.056) for the area under the curve (AUC) computed through cross-validation. In the test set, all classification performances dropped: the best was obtained by SVM based on the selected features extracted from the whole tumor lesion constructed by merging the three ROIs, with 64.2% (95% confidence interval: 52.8-74.6%) accuracy and 0.572 (0.439-0.705) for AUC. The performances did not change when the patients' age and sex were included with the radiomic features into the models. Our study confirms the presence of a subtle association between imaging characteristics and MGMT promoter methylation status. However, further verification of the strength of this association is needed, as the low diagnostic performance obtained in this validation cohort is not sufficiently robust to allow clinically meaningful predictions.

Artificial intelligence in neuro-oncology

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and structured atrous recurrent convolutional neural network

Objective.Existing radiomic methods tend to treat each isolated tumor as an inseparable whole, when extracting radiomic features. However, they may discard the critical intra-tumor metabolic heterogeneity (ITMH) information, that contributes to triggering tumor subtypes. To improve lymphoma classification performance, we propose a pseudo spatial-temporal radiomic method (PST-Radiomics) based on positron emission tomography computed tomography (PET/CT).Approach.Specifically, to enable exploitation of ITMH, we first present a multi-threshold gross tumor volume sequence (GTVS). Next, we extract 1D radiomic features based on PET images and each volume in GTVS and create a pseudo spatial-temporal feature sequence (PSTFS) tightly interwoven with ITMH. Then, we reshape PSTFS to create 2D pseudo spatial-temporal feature maps (PSTFM), of which the columns are elements of PSTFS. Finally, to learn from PSTFM in an end-to-end manner, we build a light-weighted pseudo spatial-temporal radiomic network (PSTR-Net), in which a structured atrous recurrent convolutional neural network serves as a PET branch to better exploit the strong local dependencies in PSTFM, and a residual convolutional neural network is used as a CT branch to exploit conventional radiomic features extracted from CT volumes.Main results.We validate PST-Radiomics based on a PET/CT lymphoma subtype classification task. Experimental results quantitatively demonstrate the superiority of PST-Radiomics, when compared to existing radiomic methods.Significance.Feature map visualization of our method shows that it performs complex feature selection while extracting hierarchical feature maps, which qualitatively demonstrates its superiority.

MRI radiomics and potential applications to glioblastoma

MRI plays an important role in the evaluation of glioblastoma, both at initial diagnosis and follow up after treatment. Quantitative analysis via radiomics can augment the interpretation of MRI in terms of providing insights regarding the differential diagnosis, genotype, treatment response, and prognosis. The various MRI radiomic features of glioblastoma are reviewed in this article.

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.

Immunohistochemical Expression of PD-L1 and IDH1 with Detection of MGMT Promoter Methylation in Astrocytoma

OBJECTIVE

Programmed death ligand 1 (PD-L1) expression was suggested as a poor prognostic predictor for glioblastoma. While isocitrate dehydrogenase (IDH) has been linked to enhanced overall survival in glioma cells. In glioblastoma patients receiving treatment with alkylating drugs, the methylguanine-DNA methyltransferase (MGMT) promoter's methylation status has been discovered as a potent and distinct predictor of good survival. In this study, we aimed to investigate the expression rate of PD-L1, IDH1, and MGMT methylation in patients with different grades of  astrocytoma.

METHODS

The present retrospective study retrieved the data and archived paraffin blocks of 60 cases of astrocytoma. Immunohistochemical evaluation was done to assess the expressions of PD-L1 and IDH1, Methylation-specific-PCR was used to investigate the MGMT promoter.

RESULTS

This study included astrocytoma grade II 18% (11/60), grade III 22% (13/60), grade IV 60% (36 cases). PD-L1 expression was detected in 82% of all studied cases (49/60) while IDH1 mutant astrocytoma were 73% (44/60) & methylation was reported in 58.3% (35 cases). High grade astrocytoma showed highrer expression of PD-L1 & IDH1 but with insignificant correlation (p=0.989).

CONCLUSION

There is a relatively high expression of PD-L1 and IDH1 in patients with astrocytoma. More than half of the patients presented with MGMT promoter methylation. Further studies with larger sample size are required to investigate the association between these biomarkers and characteristics of patients with astrocytoma.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.