¹⁸F-FDG-PET-based Radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma

Glioma is one of the most malignant central nervous system (CNS) tumors, with an annual incidence of 5.26 per 100,000 individuals [1]. Alkylating agents, such as temozolomide (TMZ), induce guanine-alkyl groups to the DNA and trigger tumor cell death, and have been widely utilized in the treatment of glioma [2, 3]. This methylation damage to DNA can be remedied by a DNA repair enzyme, O⁶-methylguanine-DNA methyltransferase (MGMT), which can be epigenetically silenced according to its promoter methylation status, making the MGMT promoter methylation status a strong prognostic and predictive biomarker in glioma [3‐5] that is routinely measured in the clinical evaluation of glioma patients. However, the MGMT status is mainly assessed based on tumor samples by pyrosequencing, methylation-specific polymerase chain reaction (PCR) or methylation chip analysis [6‐8], and these methods are restricted by comparatively long detection periods and high detection costs, the existence of intratumor heterogeneity, and the unattainability of tumor samples through surgery or biopsy. Therefore, noninvasive measurement of the MGMT promoter methylation status has great clinical significance to precisely guide treatment and predict prognosis.

Radiomics, a recently emerging technique for quantifying tumor characteristics with high-throughput radiomics features, allows prediction of the tumor phenotype through mathematic models that are built with selected radiomics features [9]. Current radiomics studies in the glioma field have shown promising results in demonstrating correlations between magnetic resonance imaging (MRI) features and clinical manifestations [10], WHO grades [11], molecular characteristics [12‐15], and prognoses [16]. Specifically, Li et al. and Xi et al. predicted the MGMT promoter methylation status in glioblastoma [13, 14] and Wei et al. investigated the imaging features of WHO grade II-IV astrocytoma [15] using radiomics, suggesting the efficacy of using radiomics to predict the MGMT promoter methylation status.

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is an alternative molecular imaging technique that has been applied to tumor grading [17], surgical planning [18], recurrence identification [19], and prognosis prediction [20] in glioma. In particular, Choi et al. found that MGMT-methylated WHO grade III and IV gliomas had a significantly higher maximum tumor-to-normal tissue uptake ratio (TNR) and identified a trend of higher mean TNRs in MGMT-methylated gliomas than in MGMT-unmethylated gliomas [21]. In addition, Colavolpe et al. reported a case of multicentric glioblastoma in which the lesion showed higher MGMT expression and intense ¹⁸F-FDG uptake [22], suggesting a potential correlation between the ¹⁸F-FDG-PET results and the MGMT promoter methylation status in glioma. However, to the best of our knowledge, no studies have focused on predicting the MGMT promoter methylation status using an ¹⁸F-FDG-PET-based radiomics approach. Since the MGMT promoter methylation status has been proven to be an independent prognostic and predictive marker in glioma regardless of the WHO classification or chemotherapy regimen [3‐5, 23, 24], prediction of the MGMT promoter methylation status using ¹⁸F-FDG-PET radiomics may have great clinical potential.

This study retrospectively investigated the radiomics characteristics of gliomas by ¹⁸F-FDG-PET to build a conceivable model for predicting the MGMT promoter methylation status and patient prognosis noninvasively.

In this study, ¹⁸F-FDG-PET radiomics features were extracted, selected and analyzed, and three prediction signatures, respectively, and a radiomics signature, a clinical signature, and a fusion signature, were built to predict the MGMT promoter methylation status. The radiomics signature displayed the best performance, with an accuracy of 91.3% and an AUC of 0.94 in the primary cohort, and an accuracy of 77.8% and an AUC of 0.86 in the validation cohort, respectively. The clinical value of the radiomics signature was further demonstrated by the prognosis analysis. These results suggest that ¹⁸F-FDG-PET-based radiomics is a promising method for predicting the MGMT promoter methylation status and prognosis noninvasively, demonstrating strong potential for clinical application.

Previous studies on radiological evaluation of the MGMT promoter methylation status have mainly focused on the visual features, quantitative parameters or high-throughput radiomics features [13‐15, 31‐34] of gliomas (mostly glioblastomas) based on multimodal MRI and have reported accuracies ranging from 0.58–0.89 and AUCs ranging from 0.75–0.92 (without distinguishing training and validation data). Our prediction model demonstrated comparable accuracy and AUC values, suggesting the capability of ¹⁸F-FDG-PET radiomics to predict the MGMT promoter methylation status. However, most previous studies on imaging-based prediction of the MGMT promoter methylation status have mainly focused on glioblastomas, and limited studies have included less aggressive gliomas (e.g., lower grade gliomas, such as WHO grade II and III gliomas), in which the MGMT promoter status also has prognostic and predictive value [3‐5, 23, 24]. Although there may be discriminative imaging characteristics, our ¹⁸F-FDG-PET-based radiomics signature can predict the MGMT promoter methylation status regardless of the WHO grade (e.g., in lower grade gliomas and glioblastomas) or pathological information (e.g., in astrocytomas and oligodendrogliomas), suggesting the capability of noninvasive prediction without previous knowledge based on tumor samples.

Unlike MRI, which displays the structural characteristics of tumors, PET is a highly sensitive molecular imaging technique that reflects the altered tumor metabolism that is ubiquitous among cancer cells. Malignant brain tumors usually exhibit an altered glucose metabolism, in which glucose is converted to pyruvate and further into lactate instead of entering mitochondria and the citric acid cycle [35]. ¹⁸F-FDG, a glucose analogue, can be taken up by cells but not further catabolized through glycolysis, making it a reliable radiotracer for measuring cancer cell metabolism. Considering the relationship between the glucose metabolism and oncogenic reprogramming [36], radiogenomic analysis based on ¹⁸F-FDG-PET may reflect certain molecular processes through imaging data, which is the theoretical basis of our study. However, compared with anatomical imaging modalities (e.g., CT and MRI), ¹⁸F-FDG-PET has a relatively low spatial resolution, which limits the stability and accuracy of certain features, especially in lesions with a relatively small volume [37].

Feature selection is a core step in radiomics studies since most features have little relevance to the MGMT promoter methylation status and may overwhelm the distinguishable features if they cannot be effectively reduced. The number of final selected features also needs to be balanced according to the patient cohort size because the addition of relevant features may increase performance in the primary cohort but may also result in overfitting of the radiomics signature. In our study, the Wilcoxon rank-sum test removed 1543 of the 1561 radiomics features that were irrelevant to the MGMT promoter methylation status, and logistic regression with the L1 penalty diluted the weights, allowing selection of the final 5 radiomics features to construct the radiomics signature. Although the selected radiomics features are not visually available to nuclear medicine physicians (though they are mathematically easy to comprehend), the radiomics signature did provide additional assistance to physicians in the noninvasive molecular diagnosis of glioma (Fig. 5).

Three signatures were built in our study to predict the MGMT promoter methylation status. In addition to the radiomics signature, the clinical signature was built with visualized imaging features (e.g., metabolic pattern), and the fusion signature was built with the 5 selected radiomics features and the metabolic pattern. However, the radiomics signature demonstrated the best performance and outweighed the clinical signature in both the primary and validation cohorts, suggesting that the selected radiomics features are more reliable than the clinically assessed imaging features in differentiating tumors based on the MGMT promotor methylation status. Objective clinical features (e.g., age and sex) and the most frequently used quantitative imaging parameters (e.g., SUVmax and SUVmean) were excluded by the AIC when building the clinical signature, although some of these features are the only references for physicians in noninvasively evaluating the MGMT promoter methylation status without radiomics. Moreover, the addition of the clinical feature (i.e., metabolic pattern) to the set of radiomics features decreased the AUC of the prediction model, indicating a potential disturbance to the signature with the addition of features with less relevance. Thus, clinical features may not be integrated into the noninvasive radiomics evaluation of the MGMT promoter methylation status.

The MGMT promoter has proven to be a strong prognostic biomarker in glioma. The retrospective investigation of the EORTC 26981/22981 trial demonstrated that the MGMT promoter methylation status is a favorable independent prognostic biomarker in glioblastoma [5, 6]; the NOA-04 trial and the EORTC 26951/26053/22054 trial demonstrated its prognostic value in anaplastic glioma regardless of the histopathological classification and treatment strategy [3, 23, 24]. The recently reported RTOG 0424 trial also suggests that the MGMT promotor methylation status can predict the prognosis of patients with low-grade glioma treated with radiotherapy and TMZ [4]. In accordance with previous evidences, patients with MGMT promoter methylation displayed significantly longer OS in our research. The clinical use of a radiomics signature can be further supported if the signature not only detects the MGMT promotor methylation status noninvasively but also predicts the patients’ prognosis before treatment. In our study, the radiomics signature could stratify patients into two significantly different groups based on the prognosis, suggesting the feasibility of using the radiomics signature to predict prognosis in addition to distinguishing molecular features. Moreover, the differences between the MGMT promotor methylation status-predicted and radiomics signature-predicted prognosis within each risk group were nonsignificant, even with population discrepancies within each risk group (e.g., a 20% difference in the composition of the low-risk group), indicating that the radiomics signature can serve to evaluate the prognosis aside from the MGMT promoter methylation status. Despite the results from the EORTC 26981/22981/26053/22054 and NOA-04 trials suggesting that the MGMT promoter methylation status is a predictive biomarker that can be used to evaluate whether a patient will benefit from TMZ [3, 5, 6, 24], chemotherapy strategies were not integrated into the prognosis analysis due to their diversity and the retrospective nature of this study.

The current study has several limitations. First, this was a single-center, retrospective study with a limited sample size, and the validation cohort is particularly restricted. Further prospective, multicenter studies with large patient cohorts may be essential for improving the generality and performance of the prediction model. Second, there may be a selection bias of the included patients since ¹⁸F-FDG-PET examination was not mandatorily performed. The necessity of differential diagnosis of the intracranial lesion or the evaluation of the extracranial situation were the major consideration to suggest an ¹⁸F-FDG-PET scan. Third, the radiomics model was constructed without subclassification of the metabolic pattern (i.e., solid or cystic) and therefore may not include distinguishable features for determining the MGMT promoter methylation status in each subclassification. Fourth, more than half of the patients did not reach the endpoint of the prognosis analysis, which may have introduced bias to the prognosis data. Further studies with long-term follow-up periods may be needed to eliminate such imbalances. Finally, in addition to ¹⁸F-FDG-PET data, multimodality imaging data (e.g., data from MRI and PET with alternative tracers) may be further integrated into the radiomics model for predicting the MGMT promoter methylation status in glioma.

¹⁸F-FDG-PET-based radiomics is a promising method for preoperatively evaluating the MGMT promoter methylation status in glioma and has the potential to guide the treatment and predict the prognosis of glioma patients noninvasively.