Grade Gliomas Research Articles

Impact of propofol versus sevoflurane anesthesia on molecular subtypes and immune checkpoints of glioma during surgery.

Sevoflurane and propofol are two popular anesthetics used during glioblastoma (GBM) surgery. This investigation compared the molecular subtypes and immune checkpoints of cancer cells following GBMsurgery under sevoflurane and propofol anesthesia. The expression profile data and clinical information of glioma samples of different grades were downloaded from The Cancer Genome Atlasdatabase. Weighted gene coexpression network analysiswas used to identify hub modules and key genes related to glioma grades (G2 and G3). The GEO database (GSE179004) was used to retrieve glioma surgical specimens with different anesthetic gene expression profiles. The differential expression of immune checkpoint genes under various anesthetic settings was examined using the R-ggplot2. Compared to sevoflurane, propofol significantly downregulated SERPINI1 and CAMK2A expression. These are also important factors in glioma grading. Simultaneously, SERPINI1 and CAMK2A were also significantly related to the prognosis of GBM and lower-grade gliomapatients and acted as potential tumor suppressors. In addition, propofol increases the expression of the immune checkpoint molecule, PD-L1. Our study revealed that sevoflurane can more effectively prevent the development of glioma after surgery than propofol, and SERPINI1 can be used as a new independent prognostic factor for glioma.

Simultaneously acquired rSUV and rCBF of 18F-FDG/MRI in peritumoral brain zone can help to differentiate the grade of gliomas

ObjectivesThe purpose of this study is to investigate the diagnostic performance of the peritumoral brain zone (PBZ) in differentiating glioma grades. This is accomplished by comparing the relative standardized uptake values (rSUV) and relative cerebral blood flow (rCBF) obtained from hybrid 18F-fluoro-2-deoxy-d-glucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) within different regions of interest, including the solid portion (SP) and the PBZ. MethodsTwenty-four patients with gliomas who underwent preoperative 18F-PET/MRI were enrolled in this study. The maximum standardized uptake values (SUVmax) and relative maximum cerebral blood flow (rCBFmax) were obtained from the FDG-PET and ASL data, respectively. The relative SUVmax (rSUVmax) was calculated by standardizing against the contralateral normal-appearing brain cortex. Data from the solid portion (SP) of tumor and the peritumoral brain zone (PBZ) at distance of 5 ​mm, 10 ​mm, 15 ​mm, and 20 ​mm from the SP margin were recorded. Logistic regression was used to generate receiver-operating characteristic (ROC) curves. The areas under the ROC curves (AUCs) were calculated and compared to analyze the diagnostic utility of each parameter. ResultsIn comparison to low-grade glioma (LGG), high-grade glioma (HGG) exhibited significantly higher rSUVmax and rCBFmax values in both the SP and the proximal PBZ (P ​< ​0.05). Among the various distance parameters and their combinations, the single parameter rSUVmax-SP demonstrated the highest diagnostic efficacy with an AUC of 0.788 (P ​< ​0.05). However, the AUC of rSUVmax-SP did not show a significantly improvement when combined with PBZs (P ​> ​0.05). When combining PBZs and SP with rSUVmax and rCBFmax, the rSUVmax and rCBFmax values of SP to PBZ 20 ​mm exhibited superior performance compared to single parameters and smaller regions of interest, with an AUC of 0.848. The sensitivity and specificity were determined as 73.8% and 83.6%, respectively. ConclusionThe combination of rSUVmax and rCBFmax in the SP and PBZ, based on hybrid PET/MRI, proves to be superior to using parameters solely in the SP when it comes to differentiating between HGG and LGG. Expanding the study appropriately and incorporating the use of multiple parameters can offer more valuable diagnostic information, which holds potential for clinical applications.

TRPM7 transactivates the FOSL1 gene through STAT3 and enhances glioma stemness

IntroductionWe previously reported that TRPM7 regulates glioma cells’ stemness through STAT3. In addition, we demonstrated that FOSL1 is a response gene for TRPM7, and the FOSL1 gene serves as an oncogene to promote glioma proliferation and invasion.MethodsIn the present study, we determined the effects of FOSL1 on glioma stem cell (GSC) markers CD133 and ALDH1 by flow cytometry, and the maintenance of stem cell activity by extreme limiting dilution assays (ELDA). To further gain insight into the mechanism by which TRPM7 activates transcription of the FOSL1 gene to contribute to glioma stemness, we constructed a FOSL1 promoter and its GAS mutants followed by luciferase reporter assays and ChIP-qPCR in a glioma cell line and glioma patient-derived xenoline. We further examined GSC markers ALDH1 and TRPM7 as well as FOSL1 by immunohistochemistry staining (IHC) in brain tissue microarray (TMA) of glioma patients.ResultsWe revealed that FOSL1 knockdown reduces the expression of GSC markers CD133 and ALDH1, and FOSL1 is required to maintain stem cell activity in glioma cells. The experiments also showed that mutations of − 328 to − 336 and − 378 to − 386 GAS elements markedly reduced FOSL1 promoter activity. Constitutively active STAT3 increased while dominant-negative STAT3 decreased FOSL1 promoter activity. Furthermore, overexpression of TRPM7 enhanced while silencing of TRPM7 reduced FOSL1 promoter activity. ChIP-qPCR assays revealed that STAT3, present in nuclear lysates of glioma cells stimulated by constitutively activated STAT3, can bind to two GAS elements, respectively. We demonstrated that deacetylation of FOSL1 at the Lys-116 residue located within its DNA binding domain led to an increase in FOSL1 transcriptional activity. We found that the expression of TRPM7, ALDH1, and FOSL1 protein is associated with grades of malignant glioma, and TRPM7 protein expression correlates to the expression of ALDH1 and FOSL1 in glioma patients.ConclusionsThese combined results demonstrated that TRPM7 induced FOSL1 transcriptional activation, which is mediated by the action of STAT3, a mechanism shown to be important in glioma stemness. These results indicated that FOSL1, similar to GSC markers ALDH1 and TRPM7, is a diagnostic marker and potential drug target for glioma patients.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4)

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis.

No clear conclusions have yet been reached regarding the accuracy of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade glioma (HGG) from low-grade glioma (LGG). In the present study, a meta-analysis was conducted to determine the diagnostic value of MRI radiomics in differentiating between HGG and LGG, in order to guide their clinical diagnosis. PubMed, Embase and the Cochrane Library databases were searched up to November 2022. The search included studies in which true positive, false positive, true negative and false negative values for the differentiation of HGG from LGG were reported or could be calculated by retrograde extrapolation. Duplicate publications, research without full text, studies with incomplete information or unextractable data, animal studies, reviews and systematic reviews were excluded. STATA 15.1 was used to analyze the data. The meta-analysis included 15 studies, which comprised a total of 1,124 patients, of which 701 had HGG and 423 had LGG. The pooled sensitivity and specificity of the studies overall were 0.92 (95% CI: 0.89-0.95) and 0.89 (95% CI: 0.85-0.92), respectively. The positive and negative likelihood ratios of the studies overall were 7.89 (95% CI: 6.01-10.37) and 0.09 (95% CI: 0.07-0.12), respectively. The pooled diagnostic odds ratio of the studies was 85.20 (95% CI: 54.52-133.14). The area under the summary receiver operating characteristic curve was 0.91. These findings indicate that radiomics may be an accurate tool for the differentiation of glioma grades. However, further research is needed to verify the most appropriate of these technologies.

Hsa_circRNA_0084043 promoting tumorigenesis in glioma through miR-577 sponging

Circular RNAs (circRNAs) are important non-coding RNAs (ncRNAs) involved in the development of multiple human diseases, especially cancers. circRNA_0084043 is significantly involved in the progression of melanoma. However, whether circRNA_0084043 is associated with glioma remains unknown. In this study, the upregulation of circRNA_0084043 in glioma and the association between circRNA_0084043 and glioma grade were identified. Our results showed that circRNA_0084043 is significantly involved in the proliferative, migratory, and invasive capacities of glioma cells. The results obtained from starBase, luciferase reporter assays, RNA immunoprecipitation assays, and RNA pull-down assays demonstrated that circRNA_0084043 acts as a direct sponge for miR-577. TargetScan algorithm was used to identify potential miR-577 targets, it was found that sorting nexin 5 (SNX5) is a candidate bound to miR-577. Finally, cell experiments testified that circRNA_0084043 enhanced growth, migration and invasion of glioma through the regulation of miR-577-mediated SNX5. Taken together, we concluded that circRNA_0084043 in the miR-577/SNX5 axis can be used as a candidate target for glioma therapy.

Exploiting nanopore sequencing for characterization and grading of IDH-mutant gliomas.

The 2021 WHO Classification of Central Nervous System Tumors recommended evaluation of cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion in addition to codeletion of 1p/19q to characterize IDH-mutant gliomas. Here, we demonstrated the use of a nanopore-based copy-number variation sequencing (nCNV-seq) approach to simultaneously identify deletions of CDKN2A/B and 1p/19q. The nCNV-seq approach was initially evaluated on three distinct glioma cell lines and then applied to 19 IDH-mutant gliomas (8 astrocytomas and 11 oligodendrogliomas) from patients. The whole-arm 1p/19q codeletion was detected in all oligodendrogliomas with high concordance among nCNV-seq, FISH, DNA methylation profiling, and whole-genome sequencing. For the CDKN2A/B deletion, nCNV-seq detected the loss in both astrocytoma and oligodendroglioma, with strong correlation with the CNV profiles derived from whole-genome sequencing (Pearson correlation (r) = 0.95, P < 2.2 × 10-16 to r = 0.99, P < 2.2 × 10-16 ) and methylome profiling. Furthermore, nCNV-seq can differentiate between homozygous and hemizygous deletions of CDKN2A/B. Taken together, nCNV-seq holds promise as a new, alternative approach for a rapid and simultaneous detection of the molecular signatures of IDH-mutant gliomas without capital expenditure for a sequencer.

UniVisNet: A Unified Visualization and Classification Network for accurate grading of gliomas from MRI

Accurate grading of brain tumors plays a crucial role in the diagnosis and treatment of glioma. While convolutional neural networks (CNNs) have shown promising performance in this task, their clinical applicability is still constrained by the interpretability and robustness of the models. In the conventional framework, the classification model is trained first, and then visual explanations are generated. However, this approach often leads to models that prioritize classification performance or complexity, making it difficult to achieve a precise visual explanation.Motivated by these challenges, we propose the Unified Visualization and Classification Network (UniVisNet), a novel framework that aims to improve both the classification performance and the generation of high-resolution visual explanations. UniVisNet addresses attention misalignment by introducing a subregion-based attention mechanism, which replaces traditional down-sampling operations. Additionally, multiscale feature maps are fused to achieve higher resolution, enabling the generation of detailed visual explanations. To streamline the process, we introduce the Unified Visualization and Classification head (UniVisHead), which directly generates visual explanations without the need for additional separation steps.Through extensive experiments, our proposed UniVisNet consistently outperforms strong baseline classification models and prevalent visualization methods. Notably, UniVisNet achieves remarkable results on the glioma grading task, including an AUC of 94.7%, an accuracy of 89.3%, a sensitivity of 90.4%, and a specificity of 85.3%. Moreover, UniVisNet provides visually interpretable explanations that surpass existing approaches.In conclusion, UniVisNet innovatively generates visual explanations in brain tumor grading by simultaneously improving the classification performance and generating high-resolution visual explanations. This work contributes to the clinical application of deep learning, empowering clinicians with comprehensive insights into the spatial heterogeneity of glioma.

Ceruloplasmin is associated with the infiltration of immune cells and acts as a prognostic biomarker in patients suffering from glioma.

Glioma is regarded as a prevalent form of cancer that affects the Central Nervous System (CNS), with an aggressive growth pattern and a low clinical cure rate. Despite the advancement of the treatment strategy of surgical resection, chemoradiotherapy and immunotherapy in the last decade, the clinical outcome is still grim, which is ascribed to the low immunogenicity and tumor microenvironment (TME) of glioma. The multifunctional molecule, called ceruloplasmin (CP) is involved in iron metabolism. Its expression pattern, prognostic significance, and association with the immune cells in gliomas have not been thoroughly investigated. Studies using a variety of databases, including Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and Gliovis, showed that the mRNA and protein expression levels of CP in patients suffering from glioma increased significantly with an increasing glioma grade. Kaplan-Meier (KM) curves and statistical tests highlighted a significant reduction in survival time of patients with elevated CP expression levels. According to Cox regression analysis, CP can be utilized as a stand-alone predictive biomarker in patients suffering from glioma. A significant association between CP expression and numerous immune-related pathways was found after analyzing the data using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Tumor Immune Estimation Resource (TIMER) and CIBERSORT analyses indicated a substantial correlation between the CP expression and infiltration of immunocytes in the TME. Additionally, immune checkpoints and CP expression in gliomas showed a favorable correlation. According to these results, patients with glioma have better prognoses and levels of tumor immune cell infiltration when their CP expression is low. As a result, CP could be used as a probable therapeutic target for gliomas and potentially anticipate the effectiveness of immunotherapy.

Use of Radiomics Models in Preoperative Grading of Cerebral Gliomas and Comparison with Three-dimensional Arterial Spin Labelling

AimsTo build machine learning-based radiomics models to discriminate between high- (HGGs) and low-grade gliomas (LGGs) and to compare the effectiveness of three-dimensional arterial spin labelling (3D-ASL) to evaluate which is a better method. Materials and methodsWe retrospectively analysed the magnetic resonance imaging T1WI-enhanced images of 105 patients with gliomas that were pathologically confirmed in our hospital. We divided the patients into a training group and a verification group at a ratio of 8:2; 200 patients from the Brain Tumour Segmentation Challenge 2020 were selected as the test group for image segmentation, feature extraction and screening. We constructed models using multilayer perceptron (MLP), support vector machine, random forest and logistic regression and evaluated their predictive performance. We obtained the mean maximum relative cerebral blood flow (rCBFmax) value from 3D-ASL of 105 patients from the hospital to evaluate its efficacy in discriminating between HGGs and LGGs. ResultsIn machine learning, the MLP classifier model exhibited the best performance in discriminating between HGGs and LGGs; the areas under the curve obtained by MLP and rCBFmax were 0.968 versus 0.815 (verification group) and 0.981 versus 0.815 (test group), respectively. The machine learning-based MLP classifier model performed better in discriminating between HGGs and LGGs than 3D-ASL. ConclusionIn our study, we found that machine learning-based radiomics models and 3D-ASL were valuable in discriminating between HGGs and LGGs and between them, the machine learning-based MLP model had better diagnostic performance.

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.

To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)-mutant astrocytoma. Multiparametric brain MRI data and corresponding genomic information of 234 subjects (111 positives for CDKN2A/B homozygous deletion and 123 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. Two independent multi-sequence networks (ResFN-Net and FN-Net) are built on the basis of ResNet and ConvNeXt network combined with attention mechanism to classify CDKN2A/B homozygous deletion status using MR images including contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). The performance of the network is summarized by three-way cross-validation; ROC analysis is also performed. The average cross-validation accuracy (ACC) of ResFN-Net is 0.813. The average cross-validation area under curve (AUC) of ResFN-Net is 0.8804. The average cross-validation ACC and AUC of FN-Net is 0.9236 and 0.9704, respectively. Comparing all sequence combinations of the two networks (ResFN-Net and FN-Net), the sequence combination of CE-T1WI and T2WI performed the best, and the ACC and AUC were 0.8244, 0.8975 and 0.8971, 0.9574, respectively. The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma. A novel deep learning network (FN-Net) based on preoperative MRI was developed to predict the CDKN2A/B homozygous deletion status. This network has the potential to be a practical tool for the noninvasive characterization of CDKN2A/B in glioma to support personalized classification and treatment planning. • CDKN2A/B homozygous deletion status is an important marker for glioma grading and prognosis. • An MRI-based deep learning approach was developed to predict CDKN2A/B homozygous deletion status. • The predictive performance based on ConvNeXt network was better than that of ResNet network.

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.

SDPS-17 COMPREHENSIVE ANALYSIS OF CELL DIVISION CYCLE ASSOCIATED GENE FAMILY EXPRESSION PATTERN AND PROGNOSIS VALUE REVEALS CDCA2 AS A NOVEL ONCOGENE FOR GLIOMA

Abstract Glioma is the most common central nervous system tumors. Although many biomarkers have been reported for detecting glioma, the prognosis for the disease remains poor, and therefore, new biomarkers need to be identified. The cell division cycle associated proteins (CDCA) family gene is involved in a wide range of biological functions in human cancers. Nevertheless, the expression characteristics of CDCAs, and prognostic value landscape of glioma are remain unknown. Based on a wide variety of public databases and integrated several bioinformatics analysis methods, we evaluated expression dysregulations, clinical implications, DNA methylation, copy number alterations, prognostic and therapeutic values of CDCA family gene in glioma. Further, we identified crucial biomarker, CDCA2, that influence the progression of malignant glioma and poor prognosis. Thereafter, we disclosed the effect of CDCA2 knockdown on the biological function of glioma cells through various experiments in vitro. We found that the elevated mRNA expression of CDCAs in glioma. The upregulated expression of CDCAs was found to be associated with the tumor grade in glioma. The overexpression of CDCAs mRNA were correlated with the poor prognosis. Moreover, the expression level of CDCA2 was significantly higher in glioma, and the level is correlated with grade, age, IDH1 mutation and 1p19q codeletion states. Further cell experimental results supported that the ability of cell proliferation, migration, invasion and radioresistance of glioma cells was significantly decreased after downregulated CDCA2 expression, and these changes may be achieved through the RAD51 and p21/CDK2/CyclinE1 signaling pathways. These findings provide new insights into CDCAs from a bioinformatics standpoint and highlight the significance of CDCAs in glioma diagnosis and treatment. And knockdown of CDCA2 inhibits glioma cells' proliferation, migration, invasion and radioresistance. Regulation of CDCA2 on malignant progression of glioma cells may be realized through the RAD51 and p21/CDK2/CyclinE1 signaling pathways.

TIPS-08 ACTION: A RANDOMIZED PHASE 3 STUDY OF DORDAVIPRONE (ONC201) IN PATIENTS WITH NEWLY DIAGNOSED H3 K27M-MUTANT DIFFUSE GLIOMA

Abstract BACKGROUND H3 K27M-mutant diffuse midline glioma is a universally fatal malignancy primarily affecting children and young adults. While radiotherapy (RT) provides transient benefit, no effective systemic therapy is currently available and current standard of care is RT followed by monitoring. Dordaviprone (ONC201), a first-in-class imipridone, is an oral, blood-brain barrier penetrating, selective small molecule antagonist of dopamine receptor D2/3 (DRD2) and agonist of the mitochondrial protease ClpP. An integrated pooled analysis of five open-label trials previously demonstrated efficacy in dordaviprone-treated patients with recurrent disease. This phase 3 trial will be the first randomized, controlled study evaluating ONC201 in patients with H3 K27M-mutant disease. METHODS ACTION (NCT05580562) is a randomized, double-blind, placebo-controlled, parallel-group, international Phase 3 study of ONC201 in patients with newly diagnosed H3 K27M-mutant diffuse glioma. Patients who have completed standard frontline radiotherapy will be randomized 1:1:1 to receive placebo, once-weekly dordaviprone, or twice-weekly dordaviprone on two consecutive days. Primary efficacy endpoints are overall survival (OS) and progression-free survival (PFS) in all participants; PFS will be assessed by response assessment in neuro-oncology-high grade glioma by blind independent central review. Other objectives include assessments of safety, additional efficacy endpoints, clinical benefit, quality of life, pharmacokinetics, biomarkers, and healthcare resource utilization. Eligible patients will have histologically confirmed H3 K27M-mutant diffuse glioma, a Karnofsky/Lansky performance status ≥70, and completed first-line radiotherapy. Eligibility will not be restricted based on age; however, patients must be ≥10 kg at time of randomization. Patients with a primary spinal tumor, diffuse intrinsic pontine glioma, leptomeningeal disease, or cerebrospinal fluid dissemination are not eligible. ACTION is currently enrolling in the United States, with international sites opening imminently.

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study.

Background Carbon 11 (11C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBRmax and TBRmean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBRmax, TBRmean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBRmax was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBRmax groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.

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

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

Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis.

The fusion of multi-modal data, e.g., medical images and genomic profiles, can provide complementary information and further benefit disease diagnosis. However, multi-modal disease diagnosis confronts two challenges: (1) how to produce discriminative multi-modal representations by exploiting complementary information while avoiding noisy features from different modalities. (2) how to obtain an accurate diagnosis when only a single modality is available in real clinical scenarios. To tackle these two issues, we present a two-stage disease diagnostic framework. In the first multi-modal learning stage, we propose a novel Momentum-enriched Multi-Modal Low-Rank (M3LR) constraint to explore the high-order correlations and complementary information among different modalities, thus yielding more accurate multi-modal diagnosis. In the second stage, the privileged knowledge of the multi-modal teacher is transferred to the unimodal student via our proposed Discrepancy Supervised Contrastive Distillation (DSCD) and Gradient-guided Knowledge Modulation (GKM) modules, which benefit the unimodal-based diagnosis. We have validated our approach on two tasks: (i) glioma grading based on pathology slides and genomic data, and (ii) skin lesion classification based on dermoscopy and clinical images. Experimental results on both tasks demonstrate that our proposed method consistently outperforms existing approaches in both multi-modal and unimodal diagnoses.

Combining amino acid PET and MRI imaging increases accuracy to define malignant areas in adult glioma

Accurate determination of the extent and grade of adult-type diffuse gliomas is critical to patient management. In clinical practice, contrast-enhancing areas of diffuse gliomas in magnetic resonance imaging (MRI) sequences are usually used to target biopsy, surgery, and radiation therapy, but there can be discrepancies between these areas and the actual tumor extent. Here we show that adding 18F-fluoro-ethyl-tyrosine positron emission tomography (FET-PET) to MRI sequences accurately locates the most malignant areas of contrast-enhancing gliomas, potentially impacting subsequent management and outcomes. We present a prospective analysis of over 300 serial biopsy specimens from 23 patients with contrast-enhancing adult-type diffuse gliomas using a hybrid PET-MRI scanner to compare T2-weighted and contrast-enhancing MRI images with FET-PET. In all cases, we observe and confirm high FET uptake in early PET acquisitions (5–15 min after 18F-FET administration) outside areas of contrast enhancement on MRI, indicative of high-grade glioma. In 30% cases, inclusion of FET-positive sites changes the biopsy result to a higher tumor grade.

CSF-Glioma: A Causal Segmentation Framework for Accurate Grading and Subregion Identification of Gliomas.

Deep networks have shown strong performance in glioma grading; however, interpreting their decisions remains challenging due to glioma heterogeneity. To address these challenges, the proposed solution is the Causal Segmentation Framework (CSF). This framework aims to accurately predict high- and low-grade gliomas while simultaneously highlighting key subregions. Our framework utilizes a shrinkage segmentation method to identify subregions containing essential decision information. Moreover, we introduce a glioma grading module that combines deep learning and traditional approaches for precise grading. Our proposed model achieves the best performance among all models, with an AUC of 96.14%, an F1 score of 93.74%, an accuracy of 91.04%, a sensitivity of 91.83%, and a specificity of 88.88%. Additionally, our model exhibits efficient resource utilization, completing predictions within 2.31s and occupying only 0.12 GB of memory during the test phase. Furthermore, our approach provides clear and specific visualizations of key subregions, surpassing other methods in terms of interpretability. In conclusion, the Causal Segmentation Framework (CSF) demonstrates its effectiveness at accurately predicting glioma grades and identifying key subregions. The inclusion of causality in the CSF model enhances the reliability and accuracy of preoperative decision-making for gliomas. The interpretable results provided by the CSF model can assist clinicians in their assessment and treatment planning.

Feasibility of [18F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study

PurposeMRI and PET are used in neuro-oncology for the detection and characterisation of lesions for malignancy to target surgical biopsy and to plan surgical resections or stereotactic radiosurgery. The critical role of short-chain fatty acids (SCFAs) in brain tumour biology has come to the forefront. The non-metabolised SCFA radiotracer, [18F]fluoropivalate (FPIA), shows low background signal in most tissues except eliminating organs and has appropriate human dosimetry. Tumour uptake of the radiotracer is, however, unknown. We investigated the uptake characteristics of FPIA in this pilot PET/MRI study.MethodsTen adult glioma subjects were identified based on radiological features using standard-of-care MRI prior to any surgical intervention, with subsequent histopathological confirmation of glioma subtype and grade (lower-grade – LGG – and higher-grade – HGG – patients). FPIA was injected as an intravenous bolus injection (range 342–368 MBq), and dynamic PET and MRI data were acquired simultaneously over 66 min.ResultsAll patients tolerated the PET/MRI protocol. Three patients were reclassified following resection and histology. Tumour maximum standardised uptake value (SUVmax,60) increased in the order LGG (WHO grade 2) < HGG (WHO grade 3) < HGG (WHO grade 4). The net irreversible solute transfer, Ki, and influx rate constant, K1, were significantly higher in HGG (p < 0.05). Of the MRI variables studied, DCE-MRI-derived extravascular-and-extracellular volume fraction (ve) was high in tumours of WHO grade 4 compared with other grades (p < 0.05). SLC25A20 protein expression was higher in HGG compared with LGG.ConclusionTumoural FPIA PET uptake is higher in HGG compared to LGG. This study supports further investigation of FPIA PET/MRI for brain tumour imaging in a larger patient population.Clinical trial registrationClinicaltrials.gov, NCT04097535.