Brain tumours are a heterogeneous group of diseases including intrinsic tumours (mostly gliomas), extrinsic tumours (mostly meningiomas), as well as secondary brain tumours (metastatic) which initially start elsewhere in the body and are the most common brain tumour in adults. Brain tumours are among the most dreaded of all types of cancer which reduce life expectancy by on average 20 years – the highest of any cancer, and just 14% of adults survive for five years after diagnosis. Despite tremendous research efforts, there are currently no effective disease-modifying treatments or preventive strategies for brain cancer. Therefore, developing improved patient-oriented strategies for combating cancer including brain cancer, ranging from prevention, effective early detection and diagnosis, to better treatment with improved quality of life, is still one of the leading medical and societal challenges faced globally. Considering its high heterogeneity, relative rarity, limited funding and interest from the pharmaceutical industry, relatively small and fragmented research community, to date, we do not fully understand the behavior of this devastating disease.
Advances in Artificial Intelligence (AI) and biotechniques including next generation sequencing (NGS) provide powerful tools for designing novel studies to reveal brain cancer pathogenesis, developing techniques for diagnosis, prognosis and treatment, and thereby beating brain cancer ultimately.
This Research Topic aims at attracting studies that deepen our understanding of the pathogenesis of brain cancer using advanced technologies including single cell sequencing, 3D systems (organoids and bioprinting) and computational approaches in diagnosis and treatment of brain cancers. Original research, review, and case report are all welcome. Topics of interest include but are not limited to the following:
? Brain tumour microenvironment study
? Novel pre-clinical models including organoids and bioprinting models.
? Liquid biopsy biomarkers discovery for brain cancer early diagnosis
? Radiomics study for brain cancer diagnosis and prognosis
? Methods for multi-omics data integration including the application of AI/ML
? Mathematics modelling using genomics, genetics and radiomics data
Brain tumours are a heterogeneous group of diseases including intrinsic tumours (mostly gliomas), extrinsic tumours (mostly meningiomas), as well as secondary brain tumours (metastatic) which initially start elsewhere in the body and are the most common brain tumour in adults. Brain tumours are among the most dreaded of all types of cancer which reduce life expectancy by on average 20 years – the highest of any cancer, and just 14% of adults survive for five years after diagnosis. Despite tremendous research efforts, there are currently no effective disease-modifying treatments or preventive strategies for brain cancer. Therefore, developing improved patient-oriented strategies for combating cancer including brain cancer, ranging from prevention, effective early detection and diagnosis, to better treatment with improved quality of life, is still one of the leading medical and societal challenges faced globally. Considering its high heterogeneity, relative rarity, limited funding and interest from the pharmaceutical industry, relatively small and fragmented research community, to date, we do not fully understand the behavior of this devastating disease.
Advances in Artificial Intelligence (AI) and biotechniques including next generation sequencing (NGS) provide powerful tools for designing novel studies to reveal brain cancer pathogenesis, developing techniques for diagnosis, prognosis and treatment, and thereby beating brain cancer ultimately.
This Research Topic aims at attracting studies that deepen our understanding of the pathogenesis of brain cancer using advanced technologies including single cell sequencing, 3D systems (organoids and bioprinting) and computational approaches in diagnosis and treatment of brain cancers. Original research, review, and case report are all welcome. Topics of interest include but are not limited to the following:
? Brain tumour microenvironment study
? Novel pre-clinical models including organoids and bioprinting models.
? Liquid biopsy biomarkers discovery for brain cancer early diagnosis
? Radiomics study for brain cancer diagnosis and prognosis
? Methods for multi-omics data integration including the application of AI/ML
? Mathematics modelling using genomics, genetics and radiomics data
