National Brain Tumour Registry of China (NBTRC) statistical report of primary brain tumours diagnosed in China in years 2019–2020

Xiao, Dingquan; Chen, Yan; Li, Deling; Xi, Tianshu; Liu, Xianzhi; Zhu, Da‐Yuan; Huang, Guodong; Xu, Jianguo; He, Zhengwen; Wu, Anhua; Ma, Chiyuan; Jiang, Long; Shu, Kai; Ji, Hongming; Wang, Ning; Chen, Gang; Yang, Jiankai; Ma, Hui; Li, Zhiyong; Sun, Xiaochuan; Qu, Yan; Liu, Zhixiong; Jiang, Xiaofan; Tian, Chunlei; Ni, Shilei; Zhan, Renya; Chen, Liwen; Ge, Ming; Wang, Maode; Jiang, Xiaobing; Guo, Geng; Han, Zhenmin; Zhang, Chuan; Zhang, Tingrong; Dou, Changwu; Chu, Liangzhao; Wang, Pengcheng; Shao, Jianghua; Wu, Xiyue; Yu, Jing; Wang, Yu; Wu, Nan; Zhang, Rui; Zhang, Mingming; Hong, Yong Kil; Jian-zhong, Gao; Li, Yunqian; Pan, Yawen; Zhao, Bing; Ji, Nan; Shan, Guangliang; Jia, Wang; Zhang, Liwei

doi:10.1016/j.lanwpc.2023.100715

Cited by 5 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain tumors rank among the most common diseases globally. From 2019 to 2020, China recorded an average of 12,768 brain tumor patients annually on the (National Brain Tumor Registry Research Platform) NBTRC platform, a figure nearly 10 times higher than the cases reported in the past decade [1]. Cancer arises from the mutation or change of cellular function [2], resulting in an inability of cells to undergo programmed death [3].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Network Based on Target Information Enhancement

Zhou,

Yang,

Liu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Glioma is a kind of brain disease with high incidence, high recurrence rate, high mortality, and low cure rate. To obtain accurate diagnosis results of brain glioma, doctors need to manually compare the imaging results of different modalities many times, which will increase the diagnosis time and reduce the diagnostic efficiency. Image fusion technology has been widely used in recent years to obtain information on multimodal medical images. This paper proposes a novel image fusion framework, target information enhanced image fusion network (TIEF), using cross-modal learning and information enhancement techniques. The framework consists of a multi-sequence feature extraction block, a feature selection block, and a fusion block. The multi-sequence feature extraction block consists of multiple sobel dense conv leaky ReLu block (SDCL-block). SDCL-block mainly realizes the extraction of edge features, shallow features, and deep features. The feature selection block identifies the feature channels with rich texture information and strong discrimination ability through the effective combination of global information entropy criterion and feature jump connection. The feature fusion block mainly comprises multi-head and spatial attention mechanisms, which can realize the fusion of intra-modality and inter-modality features. On this basis, considering the influence of tumor spatial location and structure information on the fusion results, a loss function is designed, which is a weighted combination of texture loss, structure loss, and saliency loss so that texture information from multimodal magnetic resonance imaging (MMRI) and saliency information from different anatomical structures of the brain can be fused at the same time to improve the expression ability of features. In this paper, the TIEF algorithm is trained and validated on the MMRI and (Single-Photon Emission Computed Tomography-MRI) SPECT-MRI datasets of glioma and generalized on the (Computed Tomography-MRI) CT-MRI dataset of meningioma to verify the performance of the TIEF algorithm. In the image fusion task, quantitative results showed that TIEF exhibited optimal or suboptimal performance in information entropy, spatial frequency, and average gradient metrics. Qualitative results indicate that the fused images can highlight tumor and edematous features. A downstream image segmentation task was used for evaluation to further verify TIEF's effectiveness. TIEF achieved the best results in both (Dice similarity coefficient) Dice and (Hausdorff distance 95%) HD95 segmentation metrics. In the generalization task, quantitative results indicated that TIEF obtained more information in the meningioma dataset. In conclusion, TIEF can effectively achieve cross-domain information acquisition and fusion and has robustness and generalization ability.

show abstract

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Network Based on Target Information Enhancement

Zhou,

Yang,

Liu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In the United States, glioblastoma (diagnosed during 2013–2017) accounts for 48.6% (all ages combined) of all malignant brain and other central nervous system tumors, and five-year survival for patients diagnosed with glioblastoma in 2009–2015 is 7% (3–27%, varied by age) ( 1 ). Although the incidence of glioblastoma among primary brain tumors in different countries varies greatly (from ≈8.5% to 69%) ( 2 , 3 ), five-year relative survival estimates are comparable and are <7% overall ( 4 ). The standard of care for the treatment of glioblastoma (maximal safe resection followed by radiotherapy with temozolomide chemotherapy) has not changed much since 2005 (Stupp protocol) ( 5 , 6 ), and glioblastoma remains incurable ( 1 ) despite significant advances in our knowledge of its genetics and molecular biology over the past two decades.…”

Section: Introductionmentioning

confidence: 99%

Systemic and local immunosuppression in glioblastoma and its prognostic significance

Stepanenko,

Sosnovtseva,

Valikhov

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

The effectiveness of tumor therapy, especially immunotherapy and oncolytic virotherapy, critically depends on the activity of the host immune cells. However, various local and systemic mechanisms of immunosuppression operate in cancer patients. Tumor-associated immunosuppression involves deregulation of many components of immunity, including a decrease in the number of T lymphocytes (lymphopenia), an increase in the levels or ratios of circulating and tumor-infiltrating immunosuppressive subsets [e.g., macrophages, microglia, myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs)], as well as defective functions of subsets of antigen-presenting, helper and effector immune cell due to altered expression of various soluble and membrane proteins (receptors, costimulatory molecules, and cytokines). In this review, we specifically focus on data from patients with glioblastoma/glioma before standard chemoradiotherapy. We discuss glioblastoma-related immunosuppression at baseline and the prognostic significance of different subsets of circulating and tumor-infiltrating immune cells (lymphocytes, CD4+ and CD8+ T cells, Tregs, natural killer (NK) cells, neutrophils, macrophages, MDSCs, and dendritic cells), including neutrophil-to-lymphocyte ratio (NLR), focus on the immune landscape and prognostic significance of isocitrate dehydrogenase (IDH)-mutant gliomas, proneural, classical and mesenchymal molecular subtypes, and highlight the features of immune surveillance in the brain. All attempts to identify a reliable prognostic immune marker in glioblastoma tissue have led to contradictory results, which can be explained, among other things, by the unprecedented level of spatial heterogeneity of the immune infiltrate and the significant phenotypic diversity and (dys)functional states of immune subpopulations. High NLR is one of the most repeatedly confirmed independent prognostic factors for shorter overall survival in patients with glioblastoma and carcinoma, and its combination with other markers of the immune response or systemic inflammation significantly improves the accuracy of prediction; however, more prospective studies are needed to confirm the prognostic/predictive power of NLR. We call for the inclusion of dynamic assessment of NLR and other blood inflammatory markers (e.g., absolute/total lymphocyte count, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, and systemic immune response index) in all neuro-oncology studies for rigorous evaluation and comparison of their individual and combinatorial prognostic/predictive significance and relative superiority.

show abstract

“…Glioblastoma multiforme (GBM) is the most common type of malignant primary brain tumor, accounting for over half of all primary malignant tumors in the Central Nervous System (CNS) [ 1 , 2 , 3 ]. GBM is categorized as grade 4 (most malignant) astrocytic glioma in the World Health Organization (WHO) classification of brain tumors [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma: An Update in Pathology, Molecular Mechanisms and Biomarkers

Lan,

Li,

Zhang

2024

IJMS

View full text Add to dashboard Cite

Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumor in adults. Despite important advances in understanding the molecular pathogenesis and biology of this tumor in the past decade, the prognosis for GBM patients remains poor. GBM is characterized by aggressive biological behavior and high degrees of inter-tumor and intra-tumor heterogeneity. Increased understanding of the molecular and cellular heterogeneity of GBM may not only help more accurately define specific subgroups for precise diagnosis but also lay the groundwork for the successful implementation of targeted therapy. Herein, we systematically review the key achievements in the understanding of GBM molecular pathogenesis, mechanisms, and biomarkers in the past decade. We discuss the advances in the molecular pathology of GBM, including genetics, epigenetics, transcriptomics, and signaling pathways. We also review the molecular biomarkers that have potential clinical roles. Finally, new strategies, current challenges, and future directions for discovering new biomarkers and therapeutic targets for GBM will be discussed.

show abstract

National Brain Tumour Registry of China (NBTRC) statistical report of primary brain tumours diagnosed in China in years 2019–2020

Cited by 5 publications

References 26 publications

Multimodal Medical Image Fusion Network Based on Target Information Enhancement

Multimodal Medical Image Fusion Network Based on Target Information Enhancement

Systemic and local immunosuppression in glioblastoma and its prognostic significance

Glioblastoma: An Update in Pathology, Molecular Mechanisms and Biomarkers

Contact Info

Product

Resources

About