A Comparative Study Between Tumor Blood Vessels and Dynamic Contrast-enhanced MRI for Identifying Isocitrate Dehydrogenase Gene 1 (IDH1) Mutation Status in Glioma

Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated

The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.

Improved diagnostic confidence and tumor type prediction in adult-type diffuse glioma by multimodal imaging including DCE perfusion and diffusion kurtosis mapping - A standardized multicenter study

BACKGROUND AND PURPOSE

To evaluate the feasibility of a multimodal approach involving dynamic contrast-enhanced (DCE) perfusion imaging and diffusion kurtosis imaging (DKI) in the preoperative imaging of brain tumors in a multicenter setting, and to evaluate the effect on diagnostic confidence and accuracy for tumor grade and type prediction.

MATERIALS AND METHODS

One hundred and thirty-three patients with brain tumors were imaged in six hospitals with a standardized multimodal protocol. Standard imaging and six parameter maps derived from DCE and DKI sequences were reviewed off-site by two independent readers. Image quality and diagnostic confidence were evaluated in qualitative analyses. Quantitative analyses were performed to assess diagnostic accuracy and the performance of DKI and DCE parameters for tumor grade differentiation and molecular tumor type determination.

RESULTS

Standardized acquisition of DCE and DKI maps was feasible with excellent image quality. Diagnostic confidence was significantly improved from 85 % to 96 % (p = 0.0005) by additional review of the DCE and DKI maps. The combination of mean kurtosis and CBV was particularly advantageous for differentiating low-grade and high-grade glioma, oligodendroglial vs. astrocytic, and IDH1/2 wild type vs. mutated tumors.

CONCLUSION

A multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult-type glioma.

A novel risk score model based on fourteen chromatin regulators-based genes for predicting overall survival of patients with lower-grade gliomas

Background: Low grade gliomas(LGGs) present vexatious management issues for neurosurgeons. Chromatin regulators (CRs) are emerging as a focus of tumor research due to their pivotal role in tumorigenesis and progression. Hence, the goal of the current work was to unveil the function and value of CRs in patients with LGGs.

Methods: RNA-Sequencing and corresponding clinical data were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. A single-cell RNA-seq dataset was sourced from the Gene Expression Omnibus (GEO) database. Altogether 870 CRs were retrieved from the published articles in top academic journals. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were applied to construct the prognostic risk model. Patients were then assigned into high- and low-risk groups based on the median risk score. The Kaplan–Meier (K-M) survival curve and receiver operating characteristic curve (ROC) were performed to assess the prognostic value. Sequentially, functional enrichment, tumor immune microenvironment, tumor mutation burden, drug prediction, single cell analysis and so on were analyzed to further explore the value of CR-based signature. Finally, the expression of signature genes were validated by immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR).

Results: We successfully constructed and validated a 14 CRs-based model for predicting the prognosis of patients with LGGs. Moreover, we also found 14 CRs-based model was an independent prognostic factor. Functional analysis revealed that the differentially expressed genes were mainly enriched in tumor and immune related pathways. Subsequently, our research uncovered that LGGs patients with higher risk scores exhibited a higher TMB and were less likely to be responsive to immunotherapy. Meanwhile, the results of drug analysis offered several potential drug candidates. Furthermore, tSNE plots highlighting the magnitude of expression of the genes of interest in the cells from the scRNA-seq assay. Ultimately, transcription expression of six representative signature genes at the mRNA level was consistent with their protein expression changes.

Conclusion: Our findings provided a reliable biomarker for predicting the prognosis, which is expected to offer new insight into LGGs management and would hopefully become a promising target for future research.