Purpose

Although magnetic resonance imaging (MRI) and ¹⁸F-2-fluorodeoxyglucose-positron emission tomography (FDG-PET) are used for pre-surgical assessment of focal cortical dysplasia (FCD), they often disagree. This study aimed to identify factors that contribute to discrepancies in FCD imaging between MRI and FDG-PET.

Methods

Sixty-two patients (mean age, 18.9 years) with a FCD type I or II were retrospectively selected. These patients were visually categorized into two groups: 1) extent of PET abnormality larger than MRI abnormality and 2) vice versa or equivalent. Predictive factors of these two groups were analyzed by multivariate logistic regression. The extent of hypometabolic transient zone surrounding FCDs and their mean standardized uptake values were measured and compared by the Mann-Whitney U-test.

Results

FCDs were detected on MRI and PET in 46 and 55 patients, respectively, whereas no abnormality was detected in 4 patients. The PET hypometabolic areas were larger than the MRI abnormal areas in 26 patients (88 % in the temporal lobe), whereas the PET hypometabolic areas were equivalent or smaller than the MRI abnormal areas in 32 patients (69 % in the frontal lobe). The temporal lobe location was an independent predictor for differentiating the two groups (OR = 35.2, 95 % CI = 6.81-168.0, P < .001). The temporal lobe lesions had significantly wider transient zones and lower standardized uptake values than those in the other lobes (P < .001, both).

Conclusion

The discrepancies between MRI and FDG-PET findings of FCD were associated with temporal lobe location.

Purpose

To assess whether hypermetabolically-defined regions of interest (ROIs) on 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) could be used to evaluate physiological features and whether there are measurable differences between molecular subtypes and tumor grades.

Methods

Sixty-eight treatment-naïve glioma patients who underwent FDOPA PET and magnetic resonance imaging (MRI) were retrospectively included. Fluid-attenuated inversion recovery hyperintense regions (FLAIR_ROI) were segmented. FDOPA hypermetabolic regions (FDOPA_ROI, tumor-to-striatum ratios > 1) within FLAIR_ROI were extracted. Normalized maximum standardized uptake value (nSUV_max), volume of each ROI, and median relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) within FLAIR_ROI or FDOPA_ROI were calculated. Imaging metrics were compared using Students t or Mann-Whitney U tests. Area under the curve (AUC) of receiver-operating characteristic curves were used to determine whether imaging metrics within FLAIR_ROI or FDOPA_ROI can discriminate different molecular statuses or grades.

Results

Using either FLAIR_ROI or FDOPA_ROI, the nSUV_max and rCBV were significantly higher and the ADC was lower in isocitrate dehydrogenase (IDH) wild-type than mutant gliomas, and in higher-grade gliomas (HGGs) than lower-grade gliomas (LGGs). The FDOPA_ROI volume was significantly higher in 1p19q codeleted than non-codeleted gliomas, and in HGGs than LGGs. Although not significant, imaging metrics extracted by FDOPA_ROI discriminated molecular status and tumor grade more accurately than those extracted by FLAIR_ROI (AUC of IDH status, 0.87 vs. 0.82; 1p19q status, 0.78 vs. 0.73; grade, 0.87 vs. 0.76).

Conclusion

FDOPA hypermetabolic ROI may extract useful imaging features of gliomas, which can illuminate biological differences between different molecular status or tumor grades.

Purpose

We evaluated F-FDOPA PET and MRI characteristics in association with the molecular status and overall survival (OS) in a large number of low-grade gliomas (LGGs).

Methods

Eighty-six patients who underwent F-FDOPA PET and MRI and were diagnosed with new or recurrent LGGs were retrospectively evaluated with respect to their isocitrate dehydrogenase (IDH) and 1p19q status (10 IDH wild type, 57 mutant, 19 unknown; 1p19q status in IDH mutant: 20 noncodeleted, 37 codeleted). After segmentation of the hyperintense area on fluid-attenuated inversion recovery image (FLAIRROI), the following were calculated: normalized SUVmax (nSUVmax) of F-FDOPA relative to the striatum, F-FDOPA hypermetabolic volume (tumor-to-striatum ratios >1), FLAIRROI volume, relative cerebral blood volume, and apparent diffusion coefficient within FLAIRROI. Receiver operating characteristic curve and Cox regression analyses were performed.

Results

PET and MRI metrics combined with age predicted the IDH mutation and 1p19q codeletion statuses with sensitivities of 73% and 76% and specificities of 100% and 94%, respectively. Significant correlations were found between OS and the IDH mutation status (hazard ratio [HR] = 4.939), nSUVmax (HR = 2.827), F-FDOPA hypermetabolic volume (HR = 1.048), and FLAIRROI volume (HR = 1.006). The nSUVmax (HR = 151.6) for newly diagnosed LGGs and the F-FDOPA hypermetabolic volume (HR = 1.038) for recurrent LGGs demonstrated significant association with OS.

Conclusions

Combining F-FDOPA PET and MRI with age proved useful for predicting the molecular status in patients with LGGs, whereas the nSUVmax and F-FDOPA hypermetabolic volume may be useful for prognostication.

Background

The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[¹⁸F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsupervised, two-level clustering approach followed by support vector machine in order to classify the isocitrate dehydrogenase (IDH) status of gliomas.

Methods

Sixty-two treatment-naïve glioma patients who underwent FDOPA PET and MRI were retrospectively included. Contrast enhanced T1-weighted images, T2-weighted images, fluid-attenuated inversion recovery images, apparent diffusion coefficient maps, and relative cerebral blood volume maps, and FDOPA PET images were used for voxel-wise feature extraction. An unsupervised two-level clustering approach, including a self-organizing map followed by the K-means algorithm was used, and each class label was applied to the original images. The logarithmic ratio of labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. The area under the curve (AUC) of receiver operating characteristic curves, accuracy, and F1-socore were calculated and used as metrics for performance.

Results

The associations of multiparametric imaging values in each cluster were successfully visualized. Multiparametric images with 16-class clustering revealed the highest classification performance to differentiate IDH status with the AUC, accuracy, and F1-score of 0.81, 0.76, and 0.76, respectively.

Conclusions

Machine learning using an unsupervised two-level clustering approach followed by a support vector machine classified the IDH mutation status of gliomas, and visualized voxel-wise features from multiparametric MRI and FDOPA PET images. Unsupervised clustered features may improve the understanding of prioritizing multiparametric imaging for classifying IDH status.

Our purpose was to identify correlations between ¹⁸F-fluorodihydroxyphenylalanine (¹⁸F-FDOPA) uptake and physiologic MRI, including relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC), in gliomas with different molecular subtypes and to evaluate their prognostic values. Methods: Sixty-eight treatment-naïve glioma patients who underwent ¹⁸F-FDOPA PET and physiologic MRI were retrospectively selected (36 with isocitrate dehydrogenase wild-type [IDH_wt], 16 with mutant 1p/19q noncodeleted [IDH_m-noncodel], and 16 with mutant codeleted [IDH_m-codel]). Fluid-attenuated inversion recovery hyperintense areas were segmented and used as regions of interest. For voxelwise and patientwise analyses, Pearson correlation coefficients (r _voxelwise and r _patientwise) between the normalized SUV (nSUV), rCBV, and ADC were evaluated. Cox regression analysis was performed to investigate the associations between overall survival and r _voxelwise, maximum or median nSUV, median rCBV, or median ADC. Results: For IDH_wt and IDH_m-noncodel gliomas, nSUV demonstrated significant positive correlations with rCBV (r _voxelwise = 0.25 and 0.31, respectively; r _patientwise = 0.50 and 0.70, respectively) and negative correlations with ADC (r _voxelwise = -0.19 and -0.19, respectively; r _patientwise = -0.58 and -0.61, respectively) in both voxelwise and patientwise analyses. IDH_m-codel gliomas demonstrated a significant positive correlation between nSUV and ADC only in voxelwise analysis (r _voxelwise = 0.18). In Cox regression analysis, r _voxelwise between nSUV and rCBV (hazard ratio, 28.82) or ADC (hazard ratio, 0.085) had significant associations with overall survival for only IDH_wt gliomas. Conclusion: IDH_m-codel gliomas showed distinctive patterns of correlations between amino acid PET and physiologic MRI. Stronger correlations between nSUV and rCBV or ADC may result in a worse prognosis for IDH_wt gliomas.

Purpose

Although tumor localization and 3,4-dihydroxy-6-¹⁸F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas.

Methods

Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping.

Results

Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula.

Conclusion

Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.

Purpose

To examine whether the rate of change in maximum ¹⁸F-FDOPA PET uptake and the rate of change in non-enhancing tumor volume could predict malignant transformation and residual overall survival (OS) in low grade glioma (LGG) patients who received serial ¹⁸F-FDOPA PET and MRI scans.

Methods

27 LGG patients with ≥ 2 ¹⁸F-FDOPA PET and MRI scans between 2003 and 2016 were included. The rate of change in FLAIR volume (uL/day) and maximum normalized ¹⁸F-FDOPA specific uptake value (nSUV_max/month), were compared between histological and molecular subtypes. General linear models (GLMs) were used to integrate clinical information with MR-PET measurements to predict malignant transformation. Cox univariate and multivariable regression analyses were performed to identify imaging and clinical risk factors related to OS.

Results

A GLM using patient age, treatment, the rate of change in FLAIR and ¹⁸F-FDOPA nSUV_max could predict malignant transformation with > 67% sensitivity and specificity (AUC = 0.7556, P = 0.0248). A significant association was observed between OS and continuous rates of change in PET uptake (HR = 1.0212, P = 0.0034). Cox multivariable analysis confirmed that continuous measures of the rate of change in PET uptake was an independent predictor of OS (HR = 1.0242, P = 0.0033); however, stratification of patients based on increasing or decreasing rate of change in FLAIR (HR = 2.220, P = 0.025), PET uptake (HR = 2.148, P = 0.0311), or both FLAIR and PET (HR = 2.354, P = 0.0135) predicted OS.

Conclusions

The change in maximum normalized ¹⁸F-FDOPA PET uptake, with or without clinical information and rate of change in tumor volume, may be useful for predicting the risk of malignant transformation and estimating residual survival in patients with LGG.

This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.