ATRX status correlates with 11 C-methionine uptake in WHO grade II and III gliomas with IDH1 mutations

Multi-pool chemical exchange saturation transfer MRI in glioma grading, molecular subtyping and evaluating tumor proliferation

PURPOSE

To evaluate the performance of multi-pool Chemical exchange saturation transfer (CEST) MRI in prediction of glioma grade, isocitrate dehydrogenase (IDH) mutation, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss and Ki-67 labeling index (LI), based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5).

METHODS

95 patients with adult-type diffuse gliomas were analyzed. The amide, direct water saturation (DS), nuclear Overhauser enhancement (NOE), semi-solid magnetization transfer (MT) and amine signals were derived using Lorentzian fitting, and asymmetry-based amide proton transfer-weighted (APTwasym) signal was calculated. The mean value of tumor region was measured and intergroup differences were estimated using student-t test. The receiver operating curve (ROC) and area under the curve (AUC) analysis were used to evaluate the diagnostic performance of signals and their combinations. Spearman correlation analysis was performed to evaluate tumor proliferation.

RESULTS

The amide and DS signals were significantly higher in high-grade gliomas compared to low-grade gliomas, as well as in IDH-wildtype gliomas compared to IDH-mutant gliomas (all p < 0.001). The DS, MT and amine signals showed significantly differences between ATRX loss and retention in grade 2/3 IDH-mutant gliomas (all p < 0.05). The combination of signals showed the highest AUC in prediction of grade (0.857), IDH mutation (0.814) and ATRX loss (0.769). Additionally, the amide and DS signals were positively correlated with Ki-67 LI (both p < 0.001).

CONCLUSION

Multi-pool CEST MRI demonstrated good potential to predict glioma grade, IDH mutation, ATRX loss and Ki-67 LI.

Loss of methylthioadenosine phosphorylase immunoreactivity correlates with poor prognosis and elevated uptake of 11C-methionine in IDH-mutant astrocytoma

PURPOSE

The proximate localization of MTAP, which encodes methylthioadenosine phosphorylase, and CDKN2A/B on Chromosome 9q21 has allowed the loss of MTAP expression as a surrogate for homozygous deletion of CDKN2A/B. This study aimed to determine whether MTAP status correlates with clinical outcomes and 11C-methionine uptake in astrocytomas with IDH mutations.

METHODS

We conducted immunohistochemistry for MTAP in 30 patients with astrocytoma, IDH-mutant who underwent 11C-methionine positron emission tomography scans prior to surgical resection. The tumor-to-normal (T/N) ratio of 11C-methionine uptake was calculated using the mean standardized uptake value (SUV) for tumor and normal brain tissues. Cox regression analysis was used for multivariate survival analysis.

RESULTS

Among IDH-mutant astrocytomas, 26.7% (8/30) exhibited the loss of cytoplasmic MTAP expression, whereas 73.3% (22/30) tumors retained MTAP expression. The median progression-free survival (PFS) was significantly shorter in patients with MTAP loss than those with MTAP retention (1.88 years vs. 6.80 years, p = 0.003). The median overall survival (OS) was also shorter in patients with MTAP loss than in MTAP-retaining counterparts (5.23 years vs. 10.69 years, p = 0.019). Multivariate analysis identified MTAP status (hazard ratio (HR), 0.081) and extent of resection (HR, 0.104) as independent prognostic factors for PFS. Astrocytomas lacking cytoplasmic MTAP expression showed a significantly higher median T/N ratio for 11C-methionine uptake than tumors retaining MTAP (2.12 vs. 1.65, p = 0.012).

CONCLUSION

Our study revealed that the loss of MTAP expression correlates with poor prognosis and an elevated T/N ratio of 11C-methionine uptake in astrocytoma, IDH-mutant.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification

OBJECTIVE

The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL.

METHODS

We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (β = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion.

RESULTS

The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL.

CONCLUSIONS

The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

T2-FLAIR mismatch sign correlates with 11C-methionine uptake in lower-grade diffuse gliomas

PURPOSE

The T2-FLAIR mismatch sign is recognized as an imaging finding highly suggestive of IDH-mutant astrocytomas. This study was designed to determine whether the T2-FLAIR mismatch sign correlates with uptake of 11C-methionine in lower-grade gliomas.

METHODS

We included 78 histopathologically verified lower-grade gliomas (grade 2: 31 cases, grade 3: 47 cases) in this study. 78 patients underwent 11C-methionine positron emission tomography (MET-PET) scans and magnetic resonance (MR) imaging scans prior to histological diagnosis. The tumor-to-normal ratio (T/N) of 11C-methionine uptake was calculated by dividing the maximum standardized uptake value (SUV) for the tumor by the mean SUV of the normal brain. MR imaging scans were evaluated for the presence of the T2-FLAIR mismatch sign by three independent reviewers. We compared molecular status, the T2-FLAIR mismatch sign and 11C-methionine uptake among patients with different lower-grade glioma molecular types.

RESULTS

The 78 lower-grade gliomas were assigned to one of three molecular groups: Group A (IDH-mutant and 1p/19q non-codeleted, n = 22), Group O (IDH-mutant and 1p/19q codeleted, n = 20), and Group W (IDH wildtype, n = 36). T2-FLAIR mismatch was found in 16 cases (20.5%) that were comprised of 8 (36.4%), 0 (0%), 8 (22.2%) cases in the molecular group A, O and W, respectively. The median T/N ratio of MET-PET in tumors with T2-FLAIR mismatch was 1.50, which was significantly lower than that of tumors without T2-FLAIR mismatch (1.83, p < 0.001, Mann-Whitney U test). In the Groups A and W (excluding Group O), the median T/N ratio on MET-PET in groups A and W (but not group O) with T2-FLAIR mismatch was 1.50, which was significantly lower than that of tumors without T2-FLAIR mismatch (1.81, p = 0.002, Mann-Whitney U test).

CONCLUSION

The T2-FLAIR mismatch sign correlated with lower 11C-methionine uptake in lower grade gliomas.

Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas

This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-two patients with gliomas, who underwent preoperative T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1-weighted imaging (T1WI + C), and QSM scanning at 3.0T magnetic resonance imaging (MRI) were included in this study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold cross-validation was utilized as the training strategy (seven samples for each fold), and the ratio of sample size of the training, validation, and test dataset was 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance in differentiating glioblastomas (GBM) and other grade gliomas (OGG, grade II-III), and predicting IDH1 mutation and ATRX loss (accuracy: 0.80, 0.77, 0.60) than either T2 FLAIR (0.69, 0.57, 0.54) or T1WI + C (0.74, 0.57, 0.46). When combining three modalities, compared with any single modality, the best AUC/accuracy/F1-scores were reached in grading gliomas (OGG and GBM: 0.91/0.89/0.87, low-grade and high-grade gliomas: 0.83/0.86/0.81), predicting IDH1 mutation (0.88/0.89/0.85), and predicting ATRX loss (0.78/0.71/0.67). As a supplement to conventional MRI, DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades, IDH1 mutation, and ATRX loss.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

Multicenter clinical radiomics-integrated model based on [18F]FDG PET and multi-modal MRI predict ATRX mutation status in IDH-mutant lower-grade gliomas

OBJECTIVES

To develop a clinical radiomics-integrated model based on ¹⁸ F-fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG PET) and multi-modal MRI for predicting alpha thalassemia/mental retardation X-linked (ATRX) mutation status of IDH-mutant lower-grade gliomas (LGGs).

METHODS

One hundred and two patients (47 ATRX mutant-type, 55 ATRX wild-type) diagnosed with IDH-mutant LGGs (CNS WHO grades 1 and 2) were retrospectively enrolled. A total of 5540 radiomics features were extracted from structural MR (sMR) images (contrast-enhanced T1-weighted imaging, CE-T1WI; T2-weighted imaging, and T2WI), functional MR (fMR) images (apparent diffusion coefficient, ADC; cerebral blood volume, CBV), and metabolic PET images ([¹⁸F]FDG PET). The random forest algorithm was used to establish a clinical radiomics-integrated model, integrating the optimal multi-modal radiomics model with three clinical parameters. The predictive effectiveness of the models was evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA).

RESULTS

The optimal multi-modal model incorporated sMR (CE-T1WI), fMR (ADC), and metabolic ([¹⁸F]FDG) images ([¹⁸F]FDG PET+ADC+ CE-T1WI) with the area under curves (AUCs) in the training and test groups of 0.971 and 0.962, respectively. The clinical radiomics-integrated model, incorporating [¹⁸F]FDG PET+ADC+CE-T1WI, three clinical parameters (KPS, SFSD, and ATGR), showed the best predictive effectiveness in the training and test groups (0.987 and 0.975, respectively).

CONCLUSIONS

The clinical radiomics-integrated model with metabolic, structural, and functional information based on [¹⁸F]FDG PET and multi-modal MRI achieved promising performance for predicting the ATRX mutation status of IDH-mutant LGGs.

KEY POINTS

• The clinical radiomics-integrated model based on [¹⁸F]FDG PET and multi-modal MRI achieved promising performance for predicting ATRX mutation status in LGGs. • The study investigated the value of multicenter clinical radiomics-integrated model based on [¹⁸F]FDG PET and multi-modal MRI in LGGs regarding ATRX mutation status prediction. • The integrated model provided structural, functional, and metabolic information simultaneously and demonstrated with satisfactory calibration and discrimination in the training and test groups (0.987 and 0.975, respectively).

Update on amino acid PET of brain tumours

PURPOSE OF REVIEW

The aim of this study was to give an update on the emerging role of PET using radiolabelled amino acids in the diagnostic workup and management of patients with cerebral gliomas and brain metastases.

RECENT FINDINGS

Numerous studies have demonstrated the potential of PET using radiolabelled amino acids for differential diagnosis of brain tumours, delineation of tumour extent for treatment planning and biopsy guidance, differentiation between tumour progression and recurrence versus treatment-related changes, and for monitoring of therapy. The Response Assessment in Neuro-Oncology (RANO) working group - an international effort to develop new standardized response criteria for clinical trials in brain tumours - has recently recommended the use of amino acid PET imaging for brain tumour management in addition to MRI at every stage of disease. With the introduction of F-18 labelled amino acids, a broader clinical application has become possible, but is still hampered by the lack of regulatory approval and of reimbursement in many countries.

SUMMARY

PET using radiolabelled amino acids is a rapidly evolving method that can significantly enhance the diagnostic value of MRI in brain tumours. Current developments suggest that this imaging technique will become an indispensable tool in neuro-oncological centres in the near future.

Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low-Grade Gliomas Using Multiparametric MR Radiomic Features

BACKGROUND

Noninvasive detection of isocitrate dehydrogenase 1 mutation (IDH1(+)) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression ((ATRX(-)) are clinically meaningful for molecular stratification of low-grade gliomas (LGGs).

PURPOSE

To study a radiomic approach based on multiparametric MR for noninvasively determining molecular status of IDH1(+) and ATRX(-) in patients with LGG.

STUDY TYPE

Retrospective, radiomics.

POPULATION

Fifty-seven LGG patients with IDH1(+) (n = 36 with 19 ATRX(-) and 17 ATRX(+) patients) and IDH1(-) (n = 21).

FIELD STRENGTH/SEQUENCE

3.0T MRI / 3D arterial spin labeling (3D-ASL), T₂ /fluid-attenuated inversion recovery (T₂ FLAIR), and diffusion-weighted imaging (DWI).

ASSESSMENT

In all, 265 high-throughput radiomic features were extracted on each tumor volume of interest from T₂ FLAIR and the other three parametric maps of ASL-derived cerebral blood flow (CBF), DWI-derived apparent diffusion coefficient (ADC), and exponential ADC (eADC). Optimal feature subsets were selected as using the support vector machine with a recursive feature elimination algorithm (SVM-RFE). Receiver operating characteristic curve (ROC) analysis was employed to assess the efficiency for identifying the IDH1(+) and ATRX(-) status.

STATISTICAL TESTS

Student's t-test, chi-square test, and Fisher's exact test were applied to confirm whether intergroup significant differences exist between molecular subtypes decided by IDH1 and ATRX.

RESULTS

Optimal SVM predictive models of IDH1(+) and ATRX(-) were established using 28 features from T₂ Flair, ADC, eADC, and CBF and six features from T₂ Flair, ADC, and CBF. The accuracies/AUCs/sensitivity/specifity/PPV/NPV of predicting IDH1(+) in LGG were 94.74%/0.931/100%/85.71%/92.31%/100%, and those of predicting ATRX(-) in LGG with IDH1(+) were 91.67%/0.926/94.74%/88.24%/90.00%/93.75%, respectively.

DATA CONCLUSION

Using the optimal texture features extracted from multiple MR sequences or parametric maps, a promising stratifying strategy was acquired for predicting molecular subtypes of IDH1 and ATRX in LGGs.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;49:808-817.