Cancers of the brain and wider central nervous system (CNS) make up for a small percentage of newly diagnosed cancer cases annually, but a disproportionate number of these identified cases result in patient deaths. As these cancers rarely present with obvious initial physical signs or symptoms, they can go undetected and continue to develop unbeknownst to the patient. As with all cancers an early diagnosis and proper classification of a cancer can have massive benefits on prognosis and patient outcomes alike.
Glioblastomas are a particularly aggressive form of cancer which can develop within the brain or spinal cord, and their most common signs and symptoms are often easily mistaken to be caused by other conditions, i.e. headaches, seizures, and/or nausea and vomiting. Treatment is often difficult, and courses may often only slow the progression and relieve symptoms, which is why early diagnosis and proper preoperative differentiation can be hugely beneficial.
Cancer imaging methodologies are a very useful tool in identifying and subsequently classifying or differentiating cancers of the brain. Magnetic resonance imaging (MRI) is a fundamental imaging methodology employed across most types of cancers, and has its uses and limitations in differentiating glioblastoma from other brain and CNS cancers. Increased resolution of the MRI images, as well as incorporation of T1-weighted and T2-weight MRI sequences have provided clinicians with better tools to pre-surgically differentiate glioblastoma. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) is another common methodology employed when differentiating glioblastoma. 18F-FDG-PET plays an important role in both detecting and staging tumors, and the ability of this technique to reflect metabolic characteristics of tumor tissues at the molecular level is indispensable. A combination of these techniques are generally used in differentiating glioblastoma, and further research into novel methods as well as refinements to current technologies are vital to bolster the toolkit available to radiologists.
This Research Topic aims to attract submissions which outline the future possibilities of how imaging technologies and methodologies can be incorporated in order to pre-surgically identify, and differentiate glioblastoma so that correct courses of treatment can be defined and initiated at the earliest opportunity to optimize patient outcomes. This Research Topic invites submissions concerning, but not limited to, the following points;
-Refinements to MRI, 18F-FDG-PET, or other imaging modalities in differentiating glioblastoma from other brain or CNS malignancies
-Identification of new imaging modalities to this end
-Outlining of best imaging/radiological practices in identifying and differentiating these types of cancer
Please Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Cancers of the brain and wider central nervous system (CNS) make up for a small percentage of newly diagnosed cancer cases annually, but a disproportionate number of these identified cases result in patient deaths. As these cancers rarely present with obvious initial physical signs or symptoms, they can go undetected and continue to develop unbeknownst to the patient. As with all cancers an early diagnosis and proper classification of a cancer can have massive benefits on prognosis and patient outcomes alike.
Glioblastomas are a particularly aggressive form of cancer which can develop within the brain or spinal cord, and their most common signs and symptoms are often easily mistaken to be caused by other conditions, i.e. headaches, seizures, and/or nausea and vomiting. Treatment is often difficult, and courses may often only slow the progression and relieve symptoms, which is why early diagnosis and proper preoperative differentiation can be hugely beneficial.
Cancer imaging methodologies are a very useful tool in identifying and subsequently classifying or differentiating cancers of the brain. Magnetic resonance imaging (MRI) is a fundamental imaging methodology employed across most types of cancers, and has its uses and limitations in differentiating glioblastoma from other brain and CNS cancers. Increased resolution of the MRI images, as well as incorporation of T1-weighted and T2-weight MRI sequences have provided clinicians with better tools to pre-surgically differentiate glioblastoma. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) is another common methodology employed when differentiating glioblastoma. 18F-FDG-PET plays an important role in both detecting and staging tumors, and the ability of this technique to reflect metabolic characteristics of tumor tissues at the molecular level is indispensable. A combination of these techniques are generally used in differentiating glioblastoma, and further research into novel methods as well as refinements to current technologies are vital to bolster the toolkit available to radiologists.
This Research Topic aims to attract submissions which outline the future possibilities of how imaging technologies and methodologies can be incorporated in order to pre-surgically identify, and differentiate glioblastoma so that correct courses of treatment can be defined and initiated at the earliest opportunity to optimize patient outcomes. This Research Topic invites submissions concerning, but not limited to, the following points;
-Refinements to MRI, 18F-FDG-PET, or other imaging modalities in differentiating glioblastoma from other brain or CNS malignancies
-Identification of new imaging modalities to this end
-Outlining of best imaging/radiological practices in identifying and differentiating these types of cancer
Please Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
