Prognostic value of pre-irradiation FET PET in patients with not completely resectable IDH-wildtype glioma and minimal or absent contrast enhancement

The Role of PET/CT in Radiation Oncology for Central Nervous System Tumors

The investigation and application of PET modalities for the evaluation and treatment of patients with central nervous system (CNS) tumors continues to evolve, with anticipated increased uptake in the United States for both benign and malignant CNS tumors in the decade to come.

How to evaluate extent of resection in diffuse gliomas: from standards to new methods

PURPOSE OF REVIEW

Maximal safe tumor resection represents the current standard of care for patients with newly diagnosed diffuse gliomas. Recent efforts have highlighted the prognostic value of extent of resection measured as residual tumor volume in patients with isocitrate dehydrogenase (IDH)-wildtype and -mutant gliomas. Accurate assessment of such information therefore appears essential in the context of clinical trials as well as patient management.

RECENT FINDINGS

Current recommendations for evaluation of extent of resection rest upon standardized postoperative MRI including contrast-enhanced T1-weighted sequences, T2-weighted/fluid-attenuated-inversion-recovery sequences, and diffusion-weighted imaging to differentiate postoperative tumor volumes from ischemia and nonspecific imaging findings. In this context, correct timing of postoperative imaging within the postoperative period is of utmost importance. Advanced MRI techniques including perfusion-weighted MRI and MR-spectroscopy may add further insight when evaluating residual tumor remnants. Positron emission tomography (PET) using amino acid tracers proves beneficial in identifying metabolically active tumor beyond anatomical findings on conventional MRI.

SUMMARY

Future efforts will have to refine recommendations on postoperative assessment of residual tumor burden in respect to differences between IDH-wildtype and -mutant gliomas, and incorporate the emerging role of advanced imaging modalities like amino acid PET.

Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study-TROG 18.06

PURPOSE

The O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in Glioblastoma (FIG) trial is an Australian prospective, multi-centre study evaluating FET PET for glioblastoma patient management. FET PET imaging timepoints are pre-chemoradiotherapy (FET1), 1-month post-chemoradiotherapy (FET2), and at suspected progression (FET3). Before participant recruitment, site nuclear medicine physicians (NMPs) underwent credentialing of FET PET delineation and image interpretation.

METHODS

Sites were required to complete contouring and dynamic analysis by ≥ 2 NMPs on benchmarking cases (n = 6) assessing biological tumour volume (BTV) delineation (3 × FET1) and image interpretation (3 × FET3). Data was reviewed by experts and violations noted. BTV definition includes tumour-to-background ratio (TBR) threshold of 1.6 with crescent-shaped background contour in the contralateral normal brain. Recurrence/pseudoprogression interpretation (FET3) required assessment of maximum TBR (TBRmax), dynamic analysis (time activity curve [TAC] type, time to peak), and qualitative assessment. Intraclass correlation coefficient (ICC) assessed volume agreement, coefficient of variation (CoV) compared maximum/mean TBR (TBRmax/TBRmean) across cases, and pairwise analysis assessed spatial (Dice similarity coefficient [DSC]) and boundary agreement (Hausdorff distance [HD], mean absolute surface distance [MASD]).

RESULTS

Data was accrued from 21 NMPs (10 centres, n ≥ 2 each) and 20 underwent review. The initial pass rate was 93/119 (78.2%) and 27/30 requested resubmissions were completed. Violations were found in 25/72 (34.7%; 13/12 minor/major) of FET1 and 22/74 (29.7%; 14/8 minor/major) of FET3 reports. The primary reasons for resubmission were as follows: BTV over-contour (15/30, 50.0%), background placement (8/30, 26.7%), TAC classification (9/30, 30.0%), and image interpretation (7/30, 23.3%). CoV median and range for BTV, TBRmax, and TBRmean were 21.53% (12.00-30.10%), 5.89% (5.01-6.68%), and 5.01% (3.37-6.34%), respectively. BTV agreement was moderate to excellent (ICC = 0.82; 95% CI, 0.63-0.97) with good spatial (DSC = 0.84 ± 0.09) and boundary (HD = 15.78 ± 8.30 mm; MASD = 1.47 ± 1.36 mm) agreement.

CONCLUSION

The FIG study credentialing program has increased expertise across study sites. TBRmax and TBRmean were robust, with considerable variability in BTV delineation and image interpretation observed.

Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know

PET with amino acid tracers provides additional insight beyond MR imaging into the biology of gliomas that can be used for initial diagnosis, delineation of tumor margins, planning of surgical and radiation therapy, assessment of residual tumor, and evaluation of posttreatment response. Hybrid PET MR imaging allows the simultaneous acquisition of various PET and MR imaging parameters in a single investigation with reduced scanning time and improved anatomic localization. This review aimed to provide neuroradiologists with a concise overview of the various amino acid tracers and a practical understanding of the clinical applications of amino acid PET MR imaging in glioma management. Future perspectives in newer advances, novel radiotracers, radiomics, and cost-effectiveness are also outlined.

11C-methionine PET imaging characteristics in children with diffuse intrinsic pontine gliomas and relationship to survival and H3 K27M mutation status

PURPOSE

This study aimed to describe ¹¹C-methionine (¹¹C-MET) PET imaging characteristics in patients with paediatric diffuse intrinsic pontine glioma (DIPG) and correlate them with survival and H3 K27M mutation status.

METHODS

We retrospectively analysed 98 children newly diagnosed with DIPG who underwent ¹¹C-MET PET. PET imaging characteristics evaluated included uptake intensity, uniformity, metabolic tumour volume (MTV), and total lesion methionine uptake (TLMU). The maximum, mean, and peak of the tumour-to-background ratio (TBR), calculated as the corresponding standardised uptake values (SUV) divided by the mean reference value, were also recorded. The associations between the PET imaging characteristics and clinical outcomes in terms of progression-free survival (PFS) and overall survival (OS) and H3 K27M mutation status were assessed, respectively.

RESULTS

In univariate analysis, imaging characteristics significantly associated with shorter PFS and OS included a higher uniformity grade, higher TBRs, larger MTV, and higher TLMU. In multivariate analysis, larger MTV at diagnosis, shorter symptom duration, and no treatment were significantly correlated with shorter PFS and OS. The PET imaging features were not correlated with H3 K27M mutation status.

CONCLUSION

Although several imaging features were significantly associated with PFS and OS, only MTV, indicating the size of the active tumour, was identified as a strong independent prognostic factor.

Therapy Defining at Initial Diagnosis of Primary Brain Tumor-The Role of 18F-FET PET/CT and MRI

Primary malignant brain tumors are heterogeneous and infrequent neoplasms. Their classification, therapeutic regimen and prognosis have undergone significant development requiring the innovation of an imaging diagnostic. The performance of enhanced magnetic resonance imaging depends on blood-brain barrier function. Several studies have demonstrated the advantages of static and dynamic amino acid PET/CT providing accurate metabolic status in the neurooncological setting. The aim of our single-center retrospective study was to test the primary diagnostic role of amino acid PET/CT compared to enhanced MRI. Emphasis was placed on cases prior to intervention, therefore, a certain natural bias was inevitable. In our analysis for newly found brain tumors ¹⁸F-FET PET/CT outperformed contrast MRI and PWI in terms of sensitivity and negative predictive value (100% vs. 52.9% and 36.36%; 100% vs. 38.46% and 41.67%), in terms of positive predictive value their performance was roughly the same (84.21 % vs. 90% and 100%), whereas regarding specificity contrast MRI and PWI were superior (40% vs. 83.33% and 100%). Based on these results the superiority of ¹⁸F-FET PET/CT seems to present incremental value during the initial diagnosis. In the case of non-enhancing tumors, it should always be suggested as a therapy-determining test.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder-decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.