Diagnostic yield of simultaneous dynamic contrast-enhanced magnetic resonance perfusion measurements and [18F]FET PET in patients with suspected recurrent anaplastic astrocytoma and glioblastoma

Diagnostic algorithm for glioma grading using dynamic susceptibility contrast‑enhanced magnetic resonance perfusion and proton magnetic resonance spectroscopy

The present retrospective study aimed to investigate the diagnostic capacity of and design a diagnostic algorithm for dynamic susceptibility contrast-enhanced MRI (DSCE-MRI) and proton magnetic resonance spectroscopy (1H-MRS) in grading low-grade glioma (LGG) and high-grade glioma (HGG). This retrospective study enrolled 57 patients, of which 14 had LGG and 43 had HGG, five had World Health Organization grade 1, nine had grade 2, 20 had grade 3 and 23 had grade 4 glioma. All subjects underwent a standard 3T MRI brain tumor protocol with conventional MRI (cMRI) and advanced techniques, including DSCE-MRI and 1H-MRS. The associations of grade categorization with parameters in tumor and peritumor regions in the DSCE-MRI were examined, including tumor relative cerebral blood volume (TrCBV) and peripheral relative (Pr)CBV, as well as Tr and Pr cerebral blood flow (CBF) and 1H-MRS, including the creatine (Cr) and N-acetyl aspartate (NAA) ratios of choline (Cho), i.e. the TCho/NAA, PCho/NAA, TCho/Cr and PCho/Cr metabolite ratios. The data were compared using the Mann-Whitney U-test, independent samples t-test, Chi-square test, Fisher's exact test and receiver operating characteristic curve analyses. Decision tree analysis established an algorithm based on cutoffs for specified significant parameters. The PrCBF had the highest performance in the preoperative prediction of histological glioma grading, followed by the TrCBV, PrCBF, TrCBV, PCho/NAA, PCho/Cr, TCho/NAA and TCho/Cr. An algorithm based on TrCBV, PrCBF and TCho/Cr had a diagnostic accuracy of 100% for LGG and 90.7% for HGG and a misclassification risk of 7%. The cutoffs (sensitivity and specificity) were 2.48 (86 and 100%) for TrCBV, 1.26 (83.7 and 100%) for PrCBF and 3.18 (69.8 and 78.6%) for TCho/Cr. In conclusion, the diagnostic algorithm using TrCBV, PrCBF and TCho/Cr values, which were obtained from DSCE-MRI and 1H-MRS, increased diagnostic accuracy to 100% for LGGs and 90.7% for HGGs compared to previous studies using conventional MRI. This non-invasive advanced MRI diagnostic algorithm is recommended for clinical application for constructing preoperative strategies and prognosis of patients with glioma.

Integrated analysis of single-cell sequencing and weighted co-expression network identifies a novel signature based on cellular senescence-related genes to predict prognosis in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated.

METHODS

Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package.

RESULTS

A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted.

CONCLUSION

Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

Bibliometric and visualization analysis of radiation brain injury from 2003 to 2023

Background

Over the past two decades, the field of radiation brain injury has attracted the attention of an increasing number of brain scientists, particularly in the areas of molecular pathology and therapeutic approaches. Characterizing global collaboration networks and mapping development trends over the past 20 years is essential.

Objective

The aim of this paper is to examine significant issues and future directions while shedding light on collaboration and research status in the field of radiation brain injury.

Methods

Bibliometric studies were performed using CiteSpaceR-bibliometrix and VOSviewer software on papers regarding radiation brain injury that were published before November 2023 in the Web of Science Core Collection.

Results

In the final analysis, we found 4,913 records written in 1,219 publications by 21,529 authors from 5,007 institutions in 75 countries. There was a noticeable increase in publications in 2014 and 2021. The majority of records listed were produced by China, the United States, and other high-income countries. The largest nodes in each cluster of the collaboration network were Sun Yat-sen University, University of California-San Francisco, and the University of Toronto. Galldiks N, Barnett GH, Langen KJ and Kim JH are known to be core authors in the field. The top 3 keywords in that time frame are radiation, radiation necrosis, and radiation-therapy.

Conclusions

The objective and thorough bibliometric analysis also identifies current research hotspots and potential future paths, providing a retrospective perspective on RBI and offering useful advice to researchers choosing research topics. Future development directions include the integration of multi-omics methodologies and novel imaging techniques to improve RBI's diagnostic effectiveness and the search for new therapeutic targets.

Hybrid PET/MRI in Cerebral Glioma: Current Status and Perspectives

Advanced MRI methods and PET using radiolabelled amino acids provide valuable information, in addition to conventional MR imaging, for brain tumour diagnostics. These methods are particularly helpful in challenging situations such as the differentiation of malignant processes from benign lesions, the identification of non-enhancing glioma subregions, the differentiation of tumour progression from treatment-related changes, and the early assessment of responses to anticancer therapy. The debate over which of the methods is preferable in which situation is ongoing, and has been addressed in numerous studies. Currently, most radiology and nuclear medicine departments perform these examinations independently of each other, leading to multiple examinations for the patient. The advent of hybrid PET/MRI allowed a convergence of the methods, but to date simultaneous imaging has reached little relevance in clinical neuro-oncology. This is partly due to the limited availability of hybrid PET/MRI scanners, but is also due to the fact that PET is a second-line examination in brain tumours. PET is only required in equivocal situations, and the spatial co-registration of PET examinations of the brain to previous MRI is possible without disadvantage. A key factor for the benefit of PET/MRI in neuro-oncology is a multimodal approach that provides decisive improvements in the diagnostics of brain tumours compared with a single modality. This review focuses on studies investigating the diagnostic value of combined amino acid PET and 'advanced' MRI in patients with cerebral gliomas. Available studies suggest that the combination of amino acid PET and advanced MRI improves grading and the histomolecular characterisation of newly diagnosed tumours. Few data are available concerning the delineation of tumour extent. A clear additive diagnostic value of amino acid PET and advanced MRI can be achieved regarding the differentiation of tumour recurrence from treatment-related changes. Here, the PET-guided evaluation of advanced MR methods seems to be helpful. In summary, there is growing evidence that a multimodal approach can achieve decisive improvements in the diagnostics of cerebral gliomas, for which hybrid PET/MRI offers optimal conditions.

Hybrid 18F-Fluoroethyltyrosine PET and MRI with Perfusion to Distinguish Disease Progression from Treatment-Related Change in Malignant Brain Tumors: The Quest to Beat the Toughest Cases

Conventional MRI has important limitations when assessing for progression of disease (POD) versus treatment-related changes (TRC) in patients with malignant brain tumors. We describe the observed impact and pitfalls of implementing 18F-fluoroethyltyrosine (18F-FET) perfusion PET/MRI into routine clinical practice. Methods: Through expanded-access investigational new drug use of 18F-FET, hybrid 18F-FET perfusion PET/MRI was performed during clinical management of 80 patients with World Health Organization central nervous system grade 3 or 4 gliomas or brain metastases of 6 tissue origins for which the prior brain MRI results were ambiguous. The diagnostic performance with 18F-FET PET/MRI was dually evaluated within routine clinical service and for retrospective parametric evaluation. Various 18F-FET perfusion PET/MRI parameters were assessed, and patients were monitored for at least 6 mo to confirm the diagnosis using pathology, imaging, and clinical progress. Results: Hybrid 18F-FET perfusion PET/MRI had high overall accuracy (86%), sensitivity (86%), and specificity (87%) for difficult diagnostic cases for which conventional MRI accuracy was poor (66%). 18F-FET tumor-to-brain ratio static metrics were highly reliable for distinguishing POD from TRC (area under the curve, 0.90). Dynamic tumor-to-brain intercept was more accurate (85%) than SUV slope (73%) or time to peak (73%). Concordant PET/MRI findings were 89% accurate. When PET and MRI conflicted, 18F-FET PET was correct in 12 of 15 cases (80%), whereas MRI was correct in 3 of 15 cases (20%). Clinical management changed after 88% (36/41) of POD diagnoses, whereas management was maintained after 87% (34/39) of TRC diagnoses. Conclusion: Hybrid 18F-FET PET/MRI positively impacted the routine clinical care of challenging malignant brain tumor cases at a U.S. institution. The results add to a growing body of literature that 18F-FET PET complements MRI, even rescuing MRI when it fails.