Voxel-based 18F-FET PET segmentation and automatic clustering of tumor voxels: A significant association with IDH1 mutation status and survival in patients with gliomas

Who will benefit from vorasidenib? Review of data from the literature and open questions

The clinical efficacy of isocitrate dehydrogenase (IDH) inhibitors in the treatment of patients with grade 2 IDH-mutant (mIDH) gliomas is a significant therapeutic advancement in neuro-oncology. It expands treatment options beyond traditional radiation therapy and cytotoxic chemotherapy, which may lead to significant long-term neurotoxic effects while extending patient survival. The INDIGO study demonstrated that vorasidenib, a pan-mIDH inhibitor, improved progression-free survival for patients with grade 2 mIDH gliomas following surgical resection or biopsy compared to placebo and was well tolerated. However, these encouraging results leave a wake of unanswered questions: Will higher-grade mIDH glioma patients benefit? When is the appropriate timing to start and stop treatment? Where does this new treatment option fit in with other treatment modalities? In this study, we review the limited data available to start addressing these questions, provide a framework of how to discuss these gaps with current patients, and highlight what is needed from the neuro-oncology community for more definitive answers.

Contribution of [18F]FET PET in the Management of Gliomas, from Diagnosis to Follow-Up: A Review

Gliomas, the most common type of primary malignant brain tumors in adults, pose significant challenges in diagnosis and management due to their heterogeneity and potential aggressiveness. This review evaluates the utility of O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET), a promising imaging modality, to enhance the clinical management of gliomas. We reviewed 82 studies involving 4657 patients, focusing on the application of [18F]FET in several key areas: diagnosis, grading, identification of IDH status and presence of oligodendroglial component, guided resection or biopsy, detection of residual tumor, guided radiotherapy, detection of malignant transformation in low-grade glioma, differentiation of recurrence versus treatment-related changes and prognostic factors, and treatment response evaluation. Our findings confirm that [18F]FET helps delineate tumor tissue, improves diagnostic accuracy, and aids in therapeutic decision-making by providing crucial insights into tumor metabolism. This review underscores the need for standardized parameters and further multicentric studies to solidify the role of [18F]FET PET in routine clinical practice. By offering a comprehensive overview of current research and practical implications, this paper highlights the added value of [18F]FET PET in improving management of glioma patients from diagnosis to follow-up.

Radiosynthesis and biological evaluation of [18F]AG-120 for PET imaging of the mutant isocitrate dehydrogenase 1 in glioma

Glioma are clinically challenging tumors due to their location and invasiveness nature, which often hinder complete surgical resection. The evaluation of the isocitrate dehydrogenase mutation status has become crucial for effective patient stratification. Through a transdisciplinary approach, we have developed an 18F-labeled ligand for non-invasive assessment of the IDH1R132H variant by using positron emission tomography (PET) imaging. In this study, we have successfully prepared diastereomerically pure [18F]AG-120 by copper-mediated radiofluorination of the stannyl precursor 6 on a TRACERlab FX2 N radiosynthesis module. In vitro internalization studies demonstrated significantly higher uptake of [18F]AG-120 in U251 human high-grade glioma cells with stable overexpression of mutant IDH1 (IDH1R132H) compared to their wild-type IDH1 counterpart (0.4 vs. 0.013% applied dose/µg protein at 120 min). In vivo studies conducted in mice, exhibited the excellent metabolic stability of [18F]AG-120, with parent fractions of 85% and 91% in plasma and brain at 30 min p.i., respectively. Dynamic PET studies with [18F]AG-120 in naïve mice and orthotopic glioma rat model reveal limited blood-brain barrier permeation along with a low uptake in the brain tumor. Interestingly, there was no significant difference in uptake between mutant IDH1R132H and wild-type IDH1 tumors (tumor-to-blood ratio[40-60 min]: ~1.7 vs. ~1.3). In conclusion, our preclinical evaluation demonstrated a target-specific internalization of [18F]AG-120 in vitro, a high metabolic stability in vivo in mice, and a slightly higher accumulation of activity in IDH1R132H-glioma compared to IDH1-glioma. Overall, our findings contribute to advancing the field of molecular imaging and encourage the evaluation of [18F]AG-120 to improve diagnosis and management of glioma and other IDH1R132H-related tumors.

Evaluation of isocitrate dehydrogenase mutation in 2021 world health organization classification grade 3 and 4 glioma adult-type diffuse gliomas with 18F-fluoromisonidazole PET

PURPOSE

This study aimed to investigate the uptake characteristics of 18F-fluoromisonidazole (FMISO), in mutant-type isocitrate dehydrogenase (IDH-mutant, grade 3 and 4) and wild-type IDH (IDH-wildtype, grade 4) 2021 WHO classification adult-type diffuse gliomas.

MATERIALS AND METHODS

Patients with grade 3 and 4 adult-type diffuse gliomas (n = 35) were included in this prospective study. After registering 18F-FMISO PET and MR images, standardized uptake value (SUV) and apparent diffusion coefficient (ADC) were evaluated in hyperintense areas on fluid-attenuated inversion recovery (FLAIR) imaging (HIA), and in contrast-enhanced tumors (CET) by manually placing 3D volumes of interest. Relative SUVmax (rSUVmax) and SUVmean (rSUVmean), 10th percentile of ADC (ADC10pct), mean ADC (ADCmean) were measured in HIA and CET, respectively.

RESULTS

rSUVmean in HIA and rSUVmean in CET were significantly higher in IDH-wildtype than in IDH-mutant (P = 0.0496 and 0.03, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET, that of rSUVmax and ADC10pct in CET, that of rSUVmean in HIA and ADCmean in CET, were able to differentiate IDH-mutant from IDH-wildtype (AUC 0.80). When confined to astrocytic tumors except for oligodendroglioma, rSUVmax, rSUVmean in HIA and rSUVmean in CET were higher for IDH-wildtype than for IDH-mutant, but not significantly (P = 0.23, 0.13 and 0.14, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET was able to differentiate IDH-mutant (AUC 0.81).

CONCLUSION

PET using 18F-FMISO and ADC might provide a valuable tool for differentiating between IDH mutation status of 2021 WHO classification grade 3 and 4 adult-type diffuse gliomas.

Prediction of Glioma Grade and IDH Status Using 18F-FET PET/CT Dynamic and Multiparametric Texture Analysis

Mutations in isocitrate dehydrogenase (IDH) represent an independent predictor of better survival in patients with gliomas. We aimed to assess grade and IDH mutation status in patients with untreated gliomas, by evaluating the respective value of 18F-FET PET/CT via dynamic and texture analyses. A total of 73 patients (male: 48, median age: 47) who underwent an 18F-FET PET/CT for initial glioma evaluation were retrospectively included. IDH status was available in 61 patients (20 patients with WHO grade 2 gliomas, 41 with grade 3-4 gliomas). Time-activity curve type and 20 parameters obtained from static analysis using LIFEx© v6.30 software were recorded. Respective performance was assessed using receiver operating characteristic curve analysis and stepwise multivariate regression analysis adjusted for patients' age and sex. The time-activity curve type and texture parameters derived from the static parameters showed satisfactory-to-good performance in predicting glioma grade and IDH status. Both time-activity curve type (stepwise OR: 101.6 (95% CI: 5.76-1791), p = 0.002) and NGLDM coarseness (stepwise OR: 2.08 × 1043 (95% CI: 2.76 × 1012-1.57 × 1074), p = 0.006) were independent predictors of glioma grade. No independent predictor of IDH status was found. Dynamic and texture analyses of 18F-FET PET/CT have limited predictive value for IDH status when adjusted for confounding factors. However, they both help predict glioma grade.

A multi-label CNN model for the automatic detection and segmentation of gliomas using [18F]FET PET imaging

PURPOSE

The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [18F]fluoroethyl-L-tyrosine ([18F]FET) PET.

METHODS

Ninety-three patients (84 in-house/7 external) who underwent a 20-40-min static [18F]FET PET scan were retrospectively included. Lesions and background regions were defined by two nuclear medicine physicians using the MIM software, such that delineations by one expert reader served as ground truth for training and testing the CNN model, while delineations by the second expert reader were used to evaluate inter-reader agreement. A multi-label CNN was developed to segment the lesion and background region while a single-label CNN was implemented for a lesion-only segmentation. Lesion detectability was evaluated by classifying [18F]FET PET scans as negative when no tumor was segmented and vice versa, while segmentation performance was assessed using the dice similarity coefficient (DSC) and segmented tumor volume. The quantitative accuracy was evaluated using the maximal and mean tumor to mean background uptake ratio (TBRmax/TBRmean). CNN models were trained and tested by a threefold cross-validation (CV) using the in-house data, while the external data was used for an independent evaluation to assess the generalizability of the two CNN models.

RESULTS

Based on the threefold CV, the multi-label CNN model achieved 88.9% sensitivity and 96.5% precision for discriminating between positive and negative [18F]FET PET scans compared to a 35.3% sensitivity and 83.1% precision obtained with the single-label CNN model. In addition, the multi-label CNN allowed an accurate estimation of the maximal/mean lesion and mean background uptake, resulting in an accurate TBRmax/TBRmean estimation compared to a semi-automatic approach. In terms of lesion segmentation, the multi-label CNN model (DSC = 74.6 ± 23.1%) demonstrated equal performance as the single-label CNN model (DSC = 73.7 ± 23.2%) with tumor volumes estimated by the single-label and multi-label model (22.9 ± 23.6 ml and 23.1 ± 24.3 ml, respectively) closely approximating the tumor volumes estimated by the expert reader (24.1 ± 24.4 ml). DSCs of both CNN models were in line with the DSCs by the second expert reader compared with the lesion segmentations by the first expert reader, while detection and segmentation performance of both CNN models as determined with the in-house data were confirmed by the independent evaluation using external data.

CONCLUSION

The proposed multi-label CNN model detected positive [18F]FET PET scans with high sensitivity and precision. Once detected, an accurate tumor segmentation and estimation of background activity was achieved resulting in an automatic and accurate TBRmax/TBRmean estimation, such that user interaction and potential inter-reader variability can be minimized.

Automatic detection and delineation of pediatric gliomas on combined [18F]FET PET and MRI

Introduction

Brain and central nervous system (CNS) tumors are the second most common cancer type in children and adolescents. Positron emission tomography (PET) imaging with radiolabeled amino acids visualizes the amino acid uptake in brain tumor cells compared with the healthy brain tissue, which provides additional information over magnetic resonance imaging (MRI) for differential diagnosis, treatment planning, and the differentiation of tumor relapse from treatment-related changes. However, tumor delineation is a time-consuming task subject to inter-rater variability. We propose a deep learning method for the automatic delineation of O-(2-[18F]fluoroethyl)-l-tyrosine ([18F]FET PET) pediatric CNS tumors.

Methods

A total of 109 [18F]FET PET and MRI scans from 66 pediatric patients with manually delineated reference were included. We trained an artificial neural network (ANN) for automatic delineation and compared its performance against the manual reference on delineation accuracy and subsequent clinical metric accuracy. For clinical metrics, we extracted the biological tumor volume (BTV) and tumor-to-background mean and max (TBRmean and TBRmax).

Results

The ANN produced high tumor overlap (median dice-similarity coefficient [DSC] of 0.93). The clinical metrics extracted with the manual reference and the ANN were highly correlated (r ≥ 0.99). The spatial location of TBRmax was identical in almost all cases (96%). The ANN and the manual reference produced similar changes in the clinical metrics between baseline and follow-up scans.

Conclusion

The proposed ANN achieved high concordance with the manual reference and may be an important tool for decision aid, limiting inter-reader variance and improving longitudinal evaluation in clinical routine, and for future multicenter studies of pediatric CNS tumors.

Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms

PURPOSE

The availability of automated, accurate, and robust gross tumor volume (GTV) segmentation algorithms is critical for the management of head and neck cancer (HNC) patients. In this work, we evaluated 3 state-of-the-art deep learning algorithms combined with 8 different loss functions for PET image segmentation using a comprehensive training set and evaluated its performance on an external validation set of HNC patients.

PATIENTS AND METHODS

18F-FDG PET/CT images of 470 patients presenting with HNC on which manually defined GTVs serving as standard of reference were used for training (340 patients), evaluation (30 patients), and testing (100 patients from different centers) of these algorithms. PET image intensity was converted to SUVs and normalized in the range (0-1) using the SUVmax of the whole data set. PET images were cropped to 12 × 12 × 12 cm3 subvolumes using isotropic voxel spacing of 3 × 3 × 3 mm3 containing the whole tumor and neighboring background including lymph nodes. We used different approaches for data augmentation, including rotation (-15 degrees, +15 degrees), scaling (-20%, 20%), random flipping (3 axes), and elastic deformation (sigma = 1 and proportion to deform = 0.7) to increase the number of training sets. Three state-of-the-art networks, including Dense-VNet, NN-UNet, and Res-Net, with 8 different loss functions, including Dice, generalized Wasserstein Dice loss, Dice plus XEnt loss, generalized Dice loss, cross-entropy, sensitivity-specificity, and Tversky, were used. Overall, 28 different networks were built. Standard image segmentation metrics, including Dice similarity, image-derived PET metrics, first-order, and shape radiomic features, were used for performance assessment of these algorithms.

RESULTS

The best results in terms of Dice coefficient (mean ± SD) were achieved by cross-entropy for Res-Net (0.86 ± 0.05; 95% confidence interval [CI], 0.85-0.87), Dense-VNet (0.85 ± 0.058; 95% CI, 0.84-0.86), and Dice plus XEnt for NN-UNet (0.87 ± 0.05; 95% CI, 0.86-0.88). The difference between the 3 networks was not statistically significant (P > 0.05). The percent relative error (RE%) of SUVmax quantification was less than 5% in networks with a Dice coefficient more than 0.84, whereas a lower RE% (0.41%) was achieved by Res-Net with cross-entropy loss. For maximum 3-dimensional diameter and sphericity shape features, all networks achieved a RE ≤ 5% and ≤10%, respectively, reflecting a small variability.

CONCLUSIONS

Deep learning algorithms exhibited promising performance for automated GTV delineation on HNC PET images. Different loss functions performed competitively when using different networks and cross-entropy for Res-Net, Dense-VNet, and Dice plus XEnt for NN-UNet emerged as reliable networks for GTV delineation. Caution should be exercised for clinical deployment owing to the occurrence of outliers in deep learning-based algorithms.

Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative

RATIONALE

We evaluated K-means clustering to classify amyloid brain PETs as positive or negative.

PATIENTS AND METHODS

Sixty-six participants (31 men, 35 women; age range, 52-81 years) were recruited through a multicenter observational study: 19 cognitively normal, 25 mild cognitive impairment, and 22 dementia (11 Alzheimer disease, 3 subcortical vascular cognitive impairment, and 8 Parkinson-Lewy Body spectrum disorder). As part of the neurocognitive and imaging evaluation, each participant had an 18F-flutemetamol (Vizamyl, GE Healthcare) brain PET. All studies were processed using Cortex ID software (General Electric Company, Boston, MA) to calculate SUV ratios in 19 regions of interest and clinically interpreted by 2 dual-certified radiologists/nuclear medicine physicians, using MIM software (MIM Software Inc, Cleveland, OH), blinded to the quantitative analysis, with final interpretation based on consensus. K-means clustering was retrospectively used to classify the studies from the quantitative data.

RESULTS

Based on clinical interpretation, 46 brain PETs were negative and 20 were positive for amyloid deposition. Of 19 cognitively normal participants, 1 (5%) had a positive 18F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18F-flutemetamol brain PET; 7 of 11 participants with Alzheimer disease (64%), 1 of 3 participants with vascular cognitive impairment (33%), and 2 of 8 participants with Parkinson-Lewy Body spectrum disorder (25%) had a positive 18F-flutemetamol brain PET. Using clinical interpretation as the criterion standard, K-means clustering (K = 2) gave sensitivity of 95%, specificity of 98%, and accuracy of 97%.

CONCLUSIONS

K-means clustering may be a powerful algorithm for classifying amyloid brain PET.

A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs

The kinetic analysis of [Formula: see text]-FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of [Formula: see text]-FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4-50 min p.i.. When limiting the acquisition time to 20-40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time ≈ 1 min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001).

Artificial intelligence for molecular neuroimaging

In recent years, artificial intelligence (AI) or the study of how computers and machines can gain intelligence, has been increasingly applied to problems in medical imaging, and in particular to molecular imaging of the central nervous system. Many AI innovations in medical imaging include improving image quality, segmentation, and automating classification of disease. These advances have led to an increased availability of supportive AI tools to assist physicians in interpreting images and making decisions affecting patient care. This review focuses on the role of AI in molecular neuroimaging, primarily applied to positron emission tomography (PET) and single photon emission computed tomography (SPECT). We emphasize technical innovations such as AI in computed tomography (CT) generation for the purposes of attenuation correction and disease localization, as well as applications in neuro-oncology and neurodegenerative diseases. Limitations and future prospects for AI in molecular brain imaging are also discussed. Just as new equipment such as SPECT and PET revolutionized the field of medical imaging a few decades ago, AI and its related technologies are now poised to bring on further disruptive changes. An understanding of these new technologies and how they work will help physicians adapt their practices and succeed with these new tools.

MGMT Promoter Methylation and IDH1 Mutations Do Not Affect [18F]FDOPA Uptake in Primary Brain Tumors

The aim of our study was to investigate the effects of methylation of O⁶-methylguanine-DNA methyltransferase promoter (MGMTp) and isocitrate dehydrogenase 1 (IDH 1) mutations on amino acid metabolism evaluated with 3,4-dihydroxy-6-[¹⁸F]-fluoro-l-phenylalanine ([¹⁸F] FDOPA) positron emission tomography/computed tomography (PET/CT). Seventy-two patients with primary brain tumors were enrolled in the study (33 women and 39 men; mean age 44 ± 12 years old). All of them were subjected to PET/CT examination after surgical treatment. Of them, 29 (40.3%) were affected by grade II glioma and 43 (59.7%) by grade III. PET/CT was scored as positive or negative and standardized uptake value ratio (SUVr) was calculated as the ratio between SUVmax of the lesion vs. that of the background. Statistical analysis was performed with the Mann–Whitney U test. Methylation of MGMTp was detectable in 61 out of the 72 patients examinated. Mean SUVr in patients without methylation of MGMTp was 1.44 ± 0.38 vs. 1.35 ± 0.48 of patients with methylation (p = 0.15). Data on IDH1 mutations were available for 43 subjects; of them, 31 are IDH-mutant. Mean SUVr was 1.38 ± 0.51 in patients IDH mutant and 1.46 ± 0.56 in patients IDH wild type. MGMTp methylation and IDH1 mutations do not affect [¹⁸F] FDOPA uptake in primary brain tumors and therefore cannot be assessed or predicted by radiopharmaceutical uptake parameters.

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.

Reference values of physiological 18F-FET uptake: Implications for brain tumor discrimination

Purpose

The aim of this study was to derive reference values of 18F-fluoro-ethyl-L-tyrosine positron emission tomography (18F-FET-PET) uptake in normal brain and head structures to allow for differentiation from tumor tissue.

Materials and methods

We examined the datasets of 70 patients (median age 53 years, range 15–79), whose dynamic 18F-FET-PET was acquired between January 2016 and October 2017. Maximum standardized uptake value (SUVmax), target-to-background standardized uptake value ratio (TBR), and time activity curve (TAC) of the 18F-FET-PET were assessed in tumor tissue and in eight normal anatomic structures and compared using the t-test and Mann-Whitney U-test. Correlation analyses were performed using Pearson or Spearman coefficients, and comparisons between several variables with Pearson’s chi-squared tests and Kruskal-Wallis tests as well as the Benjamini-Hochberg correction.

Results

All analyzed structures showed an 18F-FET uptake higher than background (threshold: TBR > 1.5). The venous sinuses and cranial muscles exhibited a TBR of 2.03±0.46 (confidence interval (CI) 1.92–2.14), higher than the uptake of caudate nucleus, pineal gland, putamen, and thalamus (TBR 1.42±0.17, CI 1.38–1.47). SUVmax, TBR, and TAC showed no difference in the analyzed structures between subjects with high-grade gliomas and subjects with low-grade gliomas, except the SUV_max of the pineal gland (t-tests of the pineal gland: SUVmax: p = 0.022; TBR: p = 0.411). No significant differences were found for gender and age.

Conclusion

Normal brain tissue demonstrates increased 18F-FET uptake compared to background tissue. Two distinct clusters have been identified, comprising venous structures and gray matter with a reference uptake of up to SUV_max of 2.99 and 2.33, respectively.

Approaches to PET Imaging of Glioblastoma

Glioblastoma multiforme (GBM) is the deadliest type of brain tumor, affecting approximately three in 100,000 adults annually. Positron emission tomography (PET) imaging provides an important non-invasive method of measuring biochemically specific targets at GBM lesions. These powerful data can characterize tumors, predict treatment effectiveness, and monitor treatment. This review will discuss the PET imaging agents that have already been evaluated in GBM patients so far, and new imaging targets with promise for future use. Previously used PET imaging agents include the tracers for markers of proliferation ([11C]methionine; [18F]fluoro-ethyl-L-tyrosine, [18F]Fluorodopa,[18F]fluoro-thymidine, and [18F]clofarabine), hypoxia sensing ([18F]FMISO, [18F]FET-NIM, [18F]EF5, [18F]HX4, and [64Cu]ATSM), and ligands for inflammation. As cancer therapeutics evolve toward personalized medicine and therapies centered on tumor biomarkers, the development of complimentary selective PET agents can dramatically enhance these efforts. Newer biomarkers for GBM PET imaging are discussed, with some already in use for PET imaging other cancers and neurological disorders. These targets include Sigma 1, Sigma 2, programmed death ligand 1, poly-ADP-ribose polymerase, and isocitrate dehydrogenase. For GBM, these imaging agents come with additional considerations such as blood-brain barrier penetration, quantitative modeling approaches, and nonspecific binding.

18F-DOPA uptake does not correlate with IDH mutation status and 1p/19q co-deletion in glioma

OBJECTIVE

The role of amino acid positron emission tomography (PET) in glioma grading and outcome prognostication has not yet been well established. This is particularly true in the context of the new WHO 2016 classification, which introduced a definition of glioma subtypes primarily based on molecular fingerprints. The aim of the present study was to correlate 3,4‑dihydroxy‑6‑[¹⁸F]‑fluoro-L‑phenylalanine (F-DOPA) uptake parameters with IDH mutation, 1p/19q status, and survival outcomes in patients with glioma.

METHODS

The study population consisted of 33 patients (17 M/16 F, mean age: 46 ± 13 years) who underwent F-DOPA PET/CT for the evaluation of tumor extent before the start of chemo or radiotherapy. The presence of IDH mutation and 1p/19q status was assessed in all the cases. Tumor volume and semiquantitative uptake parameters, namely SUV_max, tumor-to-normal brain ratio and tumor-to-normal striatum ratio, were calculated for each tumor. Imaging-derived parameters were compared between patients stratified according to molecular fingerprints, using parametric or non-parametric tests, where appropriate. The Kaplan-Meier method was used to assess differences of overall survival (OS) and progression-free survival (PFS) between groups. PET parameters were also tested as prognostic factors in univariate Cox survival regression models.

RESULTS

There were 12 IDH-wild-type and 21 IDH-mutant patients. Stratification according to 1p/19q co-deletion resulted in 20 non-co-deleted and 13 co-deleted patients. Median follow-up time from PET/CT exam was 30.5 months (range 3.5-74 months). Semiquantitative uptake parameters did correlate neither with IDH mutation nor with 1p/19q status. Uptake was similar in low-grade and high-grade tumors, respectively. In addition, F-DOPA uptake parameters, macroscopic tumor volume, or tumor grade did not stratify OS, while a correlation between SUV_max and PFS was shown in the subgroup of astrocytomas. On the other hand, IDH mutation status and presence of 1p/19q co-deletion had a significant impact on survival outcomes. The prognostic value of IDH mutation status was also confirmed in the subgroup of patients with astrocytic tumors.

CONCLUSIONS

F-DOPA uptake parameters do not correlate with tumor molecular and histological characteristics. The predictive value of PET-derived parameters on outcomes of survival is limited.