Materiały źródłowe

• #1 Brain Tumors: Your Chances for Recovery (Prognosis) | Saint Luke’s Health System

  https://www.saintlukeskc.org/health-library/brain-tumors-your-chances-recovery-prognosis

  Prognosis is the word your healthcare team may use to describe your chances of recovering from cancer. Or it may mean your likely outcome from cancer and cancer treatment. […] A doctor who is most familiar with your health is in the best position to discuss your prognosis with you and explain what the statistics may mean in your case. […] If your tumor is likely to respond well to treatment, your doctor will say you have a favorable prognosis. If your tumor is likely to be hard to control, your prognosis may be less favorable. […] Your chance of recovery may depend on: The type of brain tumor, The location of the tumor, Whether the tumor can be removed completely, Your age and overall health, How the tumor responds to treatment. […] The 5-year survival rates for people with brain tumors vary widely based on tumor type, the size and location of the tumor, a person’s age, and other factors. […] You can ask your healthcare provider about survival rates and other information.

• #2 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  Survival prediction models can potentially be used to guide treatment of glioblastoma patients. […] Prognosis is generally very poor, with a median overall survival (OS) of less than 15 months, and a 5-year OS rate of only 10%, even when aggressively treated. […] Following standard therapy, OS and progression-free survival (PFS) have been shown to correlate with several patient-specific features such as age, performance status and expression of O6-methylguanine-DNA-methyltransferase (MGMT). […] The literature on imaging biomarkers for glioblastoma survival prediction is currently dominated by radiomics, an approach in which hundreds or even thousands of features are extracted from delineated tumor regions of MR images, each quantifying some shape, texture, wavelet or histogram property.

• #3 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages | Scientific Reports

  https://www.nature.com/articles/s41598-018-37387-9

  High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. […] Conventional survival prediction based on clinical information is subjective and could be inaccurate. […] This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine. […] Presurgical prognosis of the high-grade gliomas is highly desired in clinical practice for better treatment planning, but still challenging compared to low-grade gliomas. […] These findings indicate that the traditional prognosis prediction based on the simple clinical and demographical information may not be adequately accurate.

• #4 Brain Tumors: Your Chances for Recovery (Prognosis) | Saint Luke’s Health System

  https://www.saintlukeskc.org/health-library/brain-tumors-your-chances-recovery-prognosis

  Prognosis is the word your healthcare team may use to describe your chances of recovering from cancer. Or it may mean your likely outcome from cancer and cancer treatment. […] A doctor who is most familiar with your health is in the best position to discuss your prognosis with you and explain what the statistics may mean in your case. […] If your tumor is likely to respond well to treatment, your doctor will say you have a favorable prognosis. If your tumor is likely to be hard to control, your prognosis may be less favorable. […] Your chance of recovery may depend on: The type of brain tumor, The location of the tumor, Whether the tumor can be removed completely, Your age and overall health, How the tumor responds to treatment. […] The 5-year survival rates for people with brain tumors vary widely based on tumor type, the size and location of the tumor, a person’s age, and other factors. […] You can ask your healthcare provider about survival rates and other information.

• #5 Survival for brain and spinal cord tumours | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/brain-tumours/survival

  Survival for brain and spinal cord tumours depends on different factors. So no one can tell you exactly how long you will live. […] Your doctor or specialist nurse can give you more information about your own outlook (prognosis). […] Survival is different for adults and children with brain and spinal cord tumours. […] Survival depends on many factors. […] The grade is one of the most important factors for some types of tumours. But for others, the grade is much less likely to predict how the tumour might behave. Generally, fast growing (high grade) tumours are much more likely to come back after treatment than slow growing (low grade) tumours. […] Your prognosis is better if you are younger than 40. Your general health can also affect your prognosis. If you are very fit and healthy, you are likely to recover quicker from treatment.

• #6 Survival statistics for brain and spinal cord tumours | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/brain-and-spinal-cord/prognosis-and-survival/survival-statistics

  Survival statistics for brain and spinal cord tumours are very general estimates and must be interpreted very carefully. Because these statistics are based on the experience of groups of people, they cannot be used to predict a particular persons chances of survival. […] In Canada, the 5-year net survival for all brain and central nervous system tumours is 22%. This means that about 22% of people diagnosed with brain and central nervous system tumours will survive at least 5 years. […] Survival varies with each grade and particular type or subtype of brain and spinal cord tumour. The following factors can also affect survival for brain and spinal cord tumours. However, survival rates for brain tumours will vary widely, depending on the type of tumour, its grade and the location in the brain.

• #7 Survival statistics for brain and spinal cord tumours | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/brain-and-spinal-cord/prognosis-and-survival/survival-statistics

  Low-grade tumours have a more favourable prognosis than high-grade tumours. […] People younger than 65 years of age have a more favourable prognosis. […] People with a better performance status and neurological status have a more favourable prognosis. […] Tumours that can be surgically removed have a more favourable prognosis than tumours that can be partially removed or not removed. Tumours that are in parts of the brain where they cannot be easily removed by surgery have lower survival rates. […] Survival rates for those 65 or older are usually lower. […] Meningioma (grade 1) About 80% of people remain progression-free 10 years. […] Atypical meningioma (grade 2) About 35% of people remain progression-free 10 years. […] Anaplastic or malignant meningioma (grade 3) These tumours have a median survival of less than 2 years.

• #8 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  Survival prediction models can potentially be used to guide treatment of glioblastoma patients. […] Prognosis is generally very poor, with a median overall survival (OS) of less than 15 months, and a 5-year OS rate of only 10%, even when aggressively treated. […] Following standard therapy, OS and progression-free survival (PFS) have been shown to correlate with several patient-specific features such as age, performance status and expression of O6-methylguanine-DNA-methyltransferase (MGMT). […] The literature on imaging biomarkers for glioblastoma survival prediction is currently dominated by radiomics, an approach in which hundreds or even thousands of features are extracted from delineated tumor regions of MR images, each quantifying some shape, texture, wavelet or histogram property.

• #9 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  Survival prediction models can potentially be used to guide treatment of glioblastoma patients. […] Prognosis is generally very poor, with a median overall survival (OS) of less than 15 months, and a 5-year OS rate of only 10%, even when aggressively treated. […] Following standard therapy, OS and progression-free survival (PFS) have been shown to correlate with several patient-specific features such as age, performance status and expression of O6-methylguanine-DNA-methyltransferase (MGMT). […] The literature on imaging biomarkers for glioblastoma survival prediction is currently dominated by radiomics, an approach in which hundreds or even thousands of features are extracted from delineated tumor regions of MR images, each quantifying some shape, texture, wavelet or histogram property.

• #10 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  The proposed features improve the performance of survival models for both overall- and progression-free survival, compared to models based only on several previously known prognostic factors. […] Our results show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features. […] The proposed Hd95 features contain some information about where the tumor is located in the brain and its size, both of which have been studied before and shown to carry prognostic value. […] The best model for OS was achieved by combining the proposed features with the previously known prognostic clinical features: further addition of CoM, TCV, and CEV did not provide significant improvement. […] The proposed Hd95 features measure how much each brain structure is deformed compared to its expected shape in the absence of pathology, and therefore they contain information about the location of the tumor, which has been shown previously to be a prognostic factor for OS.

• #11 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images | Scientific Reports

  https://www.nature.com/articles/s41598-022-19223-3

  The proposed features improve the performance of survival models for both overall- and progression-free survival, compared to models based only on several previously known prognostic factors. […] Our results show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features. […] The proposed Hd95 features contain some information about where the tumor is located in the brain and its size, both of which have been studied before and shown to carry prognostic value. […] The best model for OS was achieved by combining the proposed features with the previously known prognostic clinical features: further addition of CoM, TCV, and CEV did not provide significant improvement. […] The proposed Hd95 features measure how much each brain structure is deformed compared to its expected shape in the absence of pathology, and therefore they contain information about the location of the tumor, which has been shown previously to be a prognostic factor for OS.

• #12 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  The proposed features improve the performance of survival models for both overall- and progression-free survival, compared to models based only on several previously known prognostic factors. […] Our results show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features. […] The proposed Hd95 features contain some information about where the tumor is located in the brain and its size, both of which have been studied before and shown to carry prognostic value. […] The best model for OS was achieved by combining the proposed features with the previously known prognostic clinical features: further addition of CoM, TCV, and CEV did not provide significant improvement. […] The proposed Hd95 features measure how much each brain structure is deformed compared to its expected shape in the absence of pathology, and therefore they contain information about the location of the tumor, which has been shown previously to be a prognostic factor for OS.

• #13 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9671967/

  The proposed features improve the performance of survival models for both overall- and progression-free survival, compared to models based only on several previously known prognostic factors. […] Our results show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features. […] The proposed Hd95 features contain some information about where the tumor is located in the brain and its size, both of which have been studied before and shown to carry prognostic value. […] The best model for OS was achieved by combining the proposed features with the previously known prognostic clinical features: further addition of CoM, TCV, and CEV did not provide significant improvement. […] The proposed Hd95 features measure how much each brain structure is deformed compared to its expected shape in the absence of pathology, and therefore they contain information about the location of the tumor, which has been shown previously to be a prognostic factor for OS.

• #14 Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics

  https://www.mdpi.com/1718-7729/31/4/165

  Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics […] The median survival remains low at only 15 months, with approximately 40% survival in the first year post diagnosis and 17% in the second year. […] The final Cox proportional-hazards model had age and six radiomic features with nonzero coefficients. […] The C-Index of the model was 0.66 (95% C.I. 0.54–0.80). […] The results of the present study indicate that predictive indices of survival in patients with GBM include both perfusion and diffusion indices, highlighting the significant role of microstructural or hemodynamic characteristics in determining patient outcomes. […] In our study, shape and first and second order radiomic features contributed to the prediction model from T1W, diffusion, and perfusion maps. […] In newly diagnosed wild-type GBM, MRI radiomic features derived from various components of the lesion on diffusion and perfusion parametric maps can predict survival in a non-invasive manner.

• #15 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages | Scientific Reports

  https://www.nature.com/articles/s41598-018-37387-9

  Instead, based on the abundant non-invasive multi-modal neuroimaging data acquired prior to any invasive examination or surgery, a more accurate prognosis model for high-grade gliomas could be established, which is of great clinical importance and could benefit both treatment planning and patient care. […] Our method is powered by popular and effective machine learning techniques, such that it is capable of extracting multi-modal and multi-channel neuroimaging features and effectively fusing them for individual OS prediction. […] The proposed feature extraction method resulted in an approximately 30% higher sensitivity and specificity, compared to the limited demographic and tumor-related features. […] The result indicates that our model can well separate subjects with long OS from those with short OS. […] Our proposed method shows its great promise in multi-modal MRI-based diagnosis or prognosis for a wider spectrum of neurological and psychiatric diseases.

• #16 Semi-supervised Outcome Prediction for a Type of Human Brain Tumour Using Partially Labeled MRS Information | SpringerLink

  https://link.springer.com/chapter/10.1007/978-3-642-04394-9_21

  The diagnosis and prognosis of human brain tumours, especially when they are aggresive, are sensitive clinical tasks that usually require non-invasive measurement techniques. […] Outcome information for aggressive tumours, in particular, is usually scarce. […] In this paper, we aim to gauge the capability of a novel semi-supervised model, SS-Geo-GTM, to infer outcome stages from a very limited amount of available stage labels and Magnetic Resonance Spectroscopy (MRS) data corresponding to Glioblastoma, which is an aggressive tumor type.

• #17

  https://link.springer.com/article/10.1007/s11060-024-04715-1

  High-grade glioma (HGG) is the most common and deadly malignant glioma of the central nervous system. […] The aim of this study is to develop models capable of predicting functional outcomes in HGG patients before surgery, facilitating improved disease management and informed patient care. […] The current work demonstrates the ability of machine learning to classify postoperative functional outcomes in HGG patients prior to surgery accurately. […] Our results suggest that both FC and the tumors location in relation to specific networks can serve as reliable predictors of functional outcomes, leading to personalized therapeutic approaches tailored to individual patients. […] The efficacy of therapeutic interventions has been limited by various factors, such as genetic heterogeneity, accelerated cellular proliferation, and treatment-resistant cells.

• #18 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages | Scientific Reports

  https://www.nature.com/articles/s41598-018-37387-9

  Instead, based on the abundant non-invasive multi-modal neuroimaging data acquired prior to any invasive examination or surgery, a more accurate prognosis model for high-grade gliomas could be established, which is of great clinical importance and could benefit both treatment planning and patient care. […] Our method is powered by popular and effective machine learning techniques, such that it is capable of extracting multi-modal and multi-channel neuroimaging features and effectively fusing them for individual OS prediction. […] The proposed feature extraction method resulted in an approximately 30% higher sensitivity and specificity, compared to the limited demographic and tumor-related features. […] The result indicates that our model can well separate subjects with long OS from those with short OS. […] Our proposed method shows its great promise in multi-modal MRI-based diagnosis or prognosis for a wider spectrum of neurological and psychiatric diseases.

• #19 Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics

  https://www.mdpi.com/1718-7729/31/4/165

  Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics […] The median survival remains low at only 15 months, with approximately 40% survival in the first year post diagnosis and 17% in the second year. […] The final Cox proportional-hazards model had age and six radiomic features with nonzero coefficients. […] The C-Index of the model was 0.66 (95% C.I. 0.54–0.80). […] The results of the present study indicate that predictive indices of survival in patients with GBM include both perfusion and diffusion indices, highlighting the significant role of microstructural or hemodynamic characteristics in determining patient outcomes. […] In our study, shape and first and second order radiomic features contributed to the prediction model from T1W, diffusion, and perfusion maps. […] In newly diagnosed wild-type GBM, MRI radiomic features derived from various components of the lesion on diffusion and perfusion parametric maps can predict survival in a non-invasive manner.

• #20 Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics

  https://www.mdpi.com/1718-7729/31/4/165

  Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics […] The median survival remains low at only 15 months, with approximately 40% survival in the first year post diagnosis and 17% in the second year. […] The final Cox proportional-hazards model had age and six radiomic features with nonzero coefficients. […] The C-Index of the model was 0.66 (95% C.I. 0.54–0.80). […] The results of the present study indicate that predictive indices of survival in patients with GBM include both perfusion and diffusion indices, highlighting the significant role of microstructural or hemodynamic characteristics in determining patient outcomes. […] In our study, shape and first and second order radiomic features contributed to the prediction model from T1W, diffusion, and perfusion maps. […] In newly diagnosed wild-type GBM, MRI radiomic features derived from various components of the lesion on diffusion and perfusion parametric maps can predict survival in a non-invasive manner.

• #21 Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

  https://www.mdpi.com/2227-9059/11/2/364

  Brain tumors (BTs) are a significant burden on people’s health and on public healthcare, due to the poor prognosis of malignant subtypes (average five-year survival of 35%). Worldwide, 308,102 new cases of primary brain and CNS cancers were diagnosed, and 251,329 people died from these malignancies in 2020. […] The Publication of the 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) has revolutionized the diagnostic workup of CNS neoplasms, making the diagnosis of a specific tumor type particularly challenging. In fact, the WHO 2021 classification has incorporated both histologic features and genetic alterations into the diagnostic framework, with fundamental prognostic and therapeutical implications. […] Ultimately, intratumoral contrast enhancement is generally considered to be associated with higher tumor grade, although certain low-grade gliomas (LGG), such as pilocytic astrocytomas, generally enhance and certain high-grade gliomas (HGG) may not.

• #22 Family

  https://patents.google.com/patent/EP1356114A2/en

  the expression profile comprises expression of TrkC. […] the expression profile can thus be determined utilizing antibodies. […] the gene expression profile is determined utilizing oligonucleotide microarrays. […] the informative genes that characterize other classification categories such as, for example, treatment outcome, can be the same or different from the informative genes that characterize brain tumor sub-types. […] the present invention is directed to a method for assigning a brain tumor sample to a treatment outcome class, comprising the steps of: determining a weighted vote for one of the classes of one or more informative genes in the sample in accordance with a model built with a weighted voting scheme, such that the magnitude of each vote depends on the expression level of the gene in said sample and on the degree of correlation of the gene’s expression with class distinction; and summing the votes to determine the winning class, such that the winning class is the treatment outcome class to which the brain tumor sample is assigned.

• #23 Family

  https://patents.google.com/patent/EP1356114A2/en

  the expression profile comprises expression of TrkC. […] the expression profile can thus be determined utilizing antibodies. […] the gene expression profile is determined utilizing oligonucleotide microarrays. […] the informative genes that characterize other classification categories such as, for example, treatment outcome, can be the same or different from the informative genes that characterize brain tumor sub-types. […] the present invention is directed to a method for assigning a brain tumor sample to a treatment outcome class, comprising the steps of: determining a weighted vote for one of the classes of one or more informative genes in the sample in accordance with a model built with a weighted voting scheme, such that the magnitude of each vote depends on the expression level of the gene in said sample and on the degree of correlation of the gene’s expression with class distinction; and summing the votes to determine the winning class, such that the winning class is the treatment outcome class to which the brain tumor sample is assigned.

• #24 Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre | Acta Neuropathologica Communications | Full Text

  https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-019-0668-8

  The introduction of the classification of brain tumours based on their DNA methylation profile has significantly changed the diagnostic approach for cases with ambiguous histology, non-informative or contradictory molecular profiles or for entities where methylation profiling provides useful information for patient risk stratification, for example in medulloblastoma and ependymoma. […] The ambiguity of traditional histopathological criteria to inform clinical oncologists on patient management, and the patients of the prognosis, called for a radically new approach for tumour diagnostics, leading to the development of a comprehensive CNS tumour reference cohort based on genome-wide DNA methylation profiles. […] We found a change of diagnosis in approximately 25% of patients, refinement in approximately 50% and confirmation of the diagnosis in 25%. In a proportion of cases where the diagnosis changed, there was a significant impact on treatment and clinical management, and in others, the provision of accurate integrated diagnosis prevented from unnecessary, potentially harmful treatment. […] Our cohort further highlights the essential role of the methylation array as a diagnostic tool in advanced brain tumour diagnostics, when integrated into the diagnostic pathway in a structured fashion as outlined in Fig. 8 and Fig. 9.

• #25 Survival for brain and spinal cord tumours | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/brain-tumours/survival

  More than 40 out of 100 people (more than 40%) survive their cancer for 1 year or more. […] Almost 15 out of 100 people (almost 15%) survive their cancer for 5 years or more. […] For diffuse astrocytoma (grade 2 astrocytoma): 45 out of 100 people (45%) survive their brain tumour for 5 years or more. […] For anaplastic astrocytoma (grade 3 astrocytoma): more than 20 out of 100 people (more than 20%) survive their brain tumour for 5 years or more. […] For glioblastoma: more than 5 out of 100 people (more than 5%) survive their brain tumour for 5 years or more. […] For all types of ependymoma in the brain and spinal cord: around 90 out of 100 people (around 90%) survive their cancer for 5 years or more. […] For malignant ependymomas: around 85 out of 100 people (around 85%) survive their brain tumour for 5 years or more.

• #26 Survival for brain and spinal cord tumours | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/brain-tumours/survival

  For benign ependymomas: around 95 out of 100 people (around 95%) survive their brain tumour for 5 years or more. […] For all grades of oligodendroglioma: almost 55 out of 100 people (almost 55%) survived their brain tumour for 5 years or more. […] Almost 70 out of 100 people (almost 70%) with a grade 1 or grade 2 brain meningioma survive their cancer for 10 years or more. […] Around 40 out of 100 people (around 40%) with a grade 3 brain meningioma survive their cancer for 10 years or more. […] For embryonal tumours in people aged 15 to 39: around 70 out of 100 people (around 70%) survive their brain tumour for 5 years or more. […] For all pineal region tumours: 80 out of 100 people (80%) survive their brain tumour for 5 years or more. […] For malignant pineal region tumours: almost 75 out of 100 people aged 15 to 39 (almost 75%) survive their brain tumour for 5 years or more.

• #27 Survival for brain and spinal cord tumours | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/brain-tumours/survival

  For benign pineal region brain tumours: more than 95 out of 100 people aged 15 to 39 (more than 95%) survive their brain tumour for 5 years or more. […] Almost 95 out of 100 people (almost 95%) survive their spinal cord tumour for 5 years or more. […] Almost 40 out of 100 people (almost 40%) survive their lymphoma for 5 years or more. […] For benign nerve sheath tumours: 99 out of 100 (99%) people survive their tumour for 5 years or more. […] For all pituitary gland tumours: more than 95 out of 100 people (more than 95%) survive their brain tumour for 5 years or more. […] For haemangiomas: 95 out of 100 people (95%) survive their tumour for 5 years or more. […] For craniopharyngiomas: around 85 out of 100 people (around 85%) survive their tumour for 5 years or more.

• #28 Survival statistics for brain and spinal cord tumours | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/brain-and-spinal-cord/prognosis-and-survival/survival-statistics

  Low-grade tumours have a more favourable prognosis than high-grade tumours. […] People younger than 65 years of age have a more favourable prognosis. […] People with a better performance status and neurological status have a more favourable prognosis. […] Tumours that can be surgically removed have a more favourable prognosis than tumours that can be partially removed or not removed. Tumours that are in parts of the brain where they cannot be easily removed by surgery have lower survival rates. […] Survival rates for those 65 or older are usually lower. […] Meningioma (grade 1) About 80% of people remain progression-free 10 years. […] Atypical meningioma (grade 2) About 35% of people remain progression-free 10 years. […] Anaplastic or malignant meningioma (grade 3) These tumours have a median survival of less than 2 years.

• #29 Survival statistics for brain and spinal cord tumours | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/brain-and-spinal-cord/prognosis-and-survival/survival-statistics

  The median progression-free survival is approximately 12.8 months with chemotherapy alone and up to 5 years with combination chemotherapy and radiation therapy. […] Median survival ranges from 724 weeks. […] Only a doctor familiar with these factors can put all of this information together with survival statistics to arrive at a prognosis.

• #30

  https://braintumourresearch.org/pages/types-of-brain-tumours-glioblastoma-multiforme-gbm?srsltid=AfmBOoqZ0nMDWWT8HdccMM1kuDb3oQ8nusSWUROW4jM1xTbTP3VK7-D1

  Glioblastoma multiforme (GBM) is a fast-growing type of tumour of the brain or spinal cord. It is the most common type of primary malignant brain tumour in adults. […] The average survival time is devastatingly short just 12-18 months. However, 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. The reason why some people survive so much longer than others is not yet clear. […] Glioblastoma is a highly aggressive and malignant form of brain cancer, and at present, it is generally considered to be incurable. […] It varies – the average survival time is devastatingly short just 12-18 months. Only 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. Less than 1% of all patients with a glioblastoma live for more than ten years, so in the majority of cases, it is fatal.

• #31 Glioblastoma Prognosis | Survival Rates

  https://www.thebraintumourcharity.org/brain-tumour-diagnosis-treatment/types-of-brain-tumour-adult/glioblastoma/glioblastoma-prognosis/

  Glioblastoma prognosis is when your doctor or medical team explains what you might expect from your diagnosis. […] Glioblastoma is a an aggressive type of brain tumour. And, the survival time for glioblastoma is sadly short on average. […] Your doctor cant be absolutely certain about what will happen to you following a brain tumour diagnosis. But, they can give you an estimate, based on your tumour type and current situation. […] The average glioblastoma survival time is 12-18 months only 25% of patients survive more than one year, and only 5% of patients survive more than five years. […] Please remember that statistics and averages cant tell you what will happen to you specifically. […] Different people approach their prognosis in different ways. […] If you are feeling unsure or worried about a glioblastoma prognosis, contact our Support team on 0808 800 0004 or at [email protected].

• #32

  https://braintumourresearch.org/pages/types-of-brain-tumours-glioblastoma-multiforme-gbm?srsltid=AfmBOoqZ0nMDWWT8HdccMM1kuDb3oQ8nusSWUROW4jM1xTbTP3VK7-D1

  Glioblastoma multiforme (GBM) is a fast-growing type of tumour of the brain or spinal cord. It is the most common type of primary malignant brain tumour in adults. […] The average survival time is devastatingly short just 12-18 months. However, 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. The reason why some people survive so much longer than others is not yet clear. […] Glioblastoma is a highly aggressive and malignant form of brain cancer, and at present, it is generally considered to be incurable. […] It varies – the average survival time is devastatingly short just 12-18 months. Only 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. Less than 1% of all patients with a glioblastoma live for more than ten years, so in the majority of cases, it is fatal.

• #33

  https://link.springer.com/article/10.1007/s11060-024-04715-1

  Consequently, the median survival rate for patients afflicted with HGGs remains low, with a median survival of merely 14 months. […] However, surgical resection of the tumor carries the risk of causing or exacerbating functional impairment, a significant concern for the clinician, as postsurgical functional preservation (e.g., quality of life) is known to correlate with overall patient outcomes. […] To that end, the capability to forecast postsurgical functional outcomes from the initial diagnosis could prove advantageous in surgical planning and for better-informing patients of their likely treatment outcomes. […] Our results indicate that these models can accurately predict postoperative functional outcomes at the time of initial diagnosis. […] These findings indicate the feasibility of achieving classification accuracy exceeding 90% before surgical intervention, using basic demographics, tumor volume, and RS-fMRI measures.

• #34

  https://braintumourresearch.org/pages/types-of-brain-tumours-glioblastoma-multiforme-gbm?srsltid=AfmBOoqZ0nMDWWT8HdccMM1kuDb3oQ8nusSWUROW4jM1xTbTP3VK7-D1

  Glioblastoma multiforme (GBM) is a fast-growing type of tumour of the brain or spinal cord. It is the most common type of primary malignant brain tumour in adults. […] The average survival time is devastatingly short just 12-18 months. However, 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. The reason why some people survive so much longer than others is not yet clear. […] Glioblastoma is a highly aggressive and malignant form of brain cancer, and at present, it is generally considered to be incurable. […] It varies – the average survival time is devastatingly short just 12-18 months. Only 25% of glioblastoma patients survive more than one year and 5% of patients survive more than five years. Less than 1% of all patients with a glioblastoma live for more than ten years, so in the majority of cases, it is fatal.

• #35 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages | Scientific Reports

  https://www.nature.com/articles/s41598-018-37387-9

  High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. […] Conventional survival prediction based on clinical information is subjective and could be inaccurate. […] This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine. […] Presurgical prognosis of the high-grade gliomas is highly desired in clinical practice for better treatment planning, but still challenging compared to low-grade gliomas. […] These findings indicate that the traditional prognosis prediction based on the simple clinical and demographical information may not be adequately accurate.

• #36

  https://link.springer.com/article/10.1007/s11060-024-04715-1

  Predicting KPS in brain tumor patients prior to treatment can serve as a foundational tool for tailoring individualized treatment plans. […] By incorporating these models into clinical practice, we stand to enhance patient care, enabling personalized treatment plans that balance quality of life with survival.

• #37

  https://link.springer.com/article/10.1007/s11060-024-04715-1

  Predicting KPS in brain tumor patients prior to treatment can serve as a foundational tool for tailoring individualized treatment plans. […] By incorporating these models into clinical practice, we stand to enhance patient care, enabling personalized treatment plans that balance quality of life with survival.

• #38 Survival statistics for brain and spinal cord tumours | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/brain-and-spinal-cord/prognosis-and-survival/survival-statistics

  Survival statistics for brain and spinal cord tumours are very general estimates and must be interpreted very carefully. Because these statistics are based on the experience of groups of people, they cannot be used to predict a particular persons chances of survival. […] In Canada, the 5-year net survival for all brain and central nervous system tumours is 22%. This means that about 22% of people diagnosed with brain and central nervous system tumours will survive at least 5 years. […] Survival varies with each grade and particular type or subtype of brain and spinal cord tumour. The following factors can also affect survival for brain and spinal cord tumours. However, survival rates for brain tumours will vary widely, depending on the type of tumour, its grade and the location in the brain.

• #39 Glioblastoma Prognosis | Survival Rates

  https://www.thebraintumourcharity.org/brain-tumour-diagnosis-treatment/types-of-brain-tumour-adult/glioblastoma/glioblastoma-prognosis/

  Glioblastoma prognosis is when your doctor or medical team explains what you might expect from your diagnosis. […] Glioblastoma is a an aggressive type of brain tumour. And, the survival time for glioblastoma is sadly short on average. […] Your doctor cant be absolutely certain about what will happen to you following a brain tumour diagnosis. But, they can give you an estimate, based on your tumour type and current situation. […] The average glioblastoma survival time is 12-18 months only 25% of patients survive more than one year, and only 5% of patients survive more than five years. […] Please remember that statistics and averages cant tell you what will happen to you specifically. […] Different people approach their prognosis in different ways. […] If you are feeling unsure or worried about a glioblastoma prognosis, contact our Support team on 0808 800 0004 or at [email protected].

• #40 Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages | Scientific Reports

  https://www.nature.com/articles/s41598-018-37387-9

  High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. […] Conventional survival prediction based on clinical information is subjective and could be inaccurate. […] This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine. […] Presurgical prognosis of the high-grade gliomas is highly desired in clinical practice for better treatment planning, but still challenging compared to low-grade gliomas. […] These findings indicate that the traditional prognosis prediction based on the simple clinical and demographical information may not be adequately accurate.

• #41 Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images | Scientific Reports

  https://www.nature.com/articles/s41598-022-19223-3

  The proposed features improve the performance of survival models for both overall- and progression-free survival, compared to models based only on several previously known prognostic factors. […] Our results show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features. […] The proposed Hd95 features contain some information about where the tumor is located in the brain and its size, both of which have been studied before and shown to carry prognostic value. […] The best model for OS was achieved by combining the proposed features with the previously known prognostic clinical features: further addition of CoM, TCV, and CEV did not provide significant improvement. […] The proposed Hd95 features measure how much each brain structure is deformed compared to its expected shape in the absence of pathology, and therefore they contain information about the location of the tumor, which has been shown previously to be a prognostic factor for OS.

• #42

  https://link.springer.com/article/10.1007/s11060-024-04715-1

  Predicting KPS in brain tumor patients prior to treatment can serve as a foundational tool for tailoring individualized treatment plans. […] By incorporating these models into clinical practice, we stand to enhance patient care, enabling personalized treatment plans that balance quality of life with survival.

• #43 Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre | Acta Neuropathologica Communications | Full Text

  https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-019-0668-8

  The introduction of the classification of brain tumours based on their DNA methylation profile has significantly changed the diagnostic approach for cases with ambiguous histology, non-informative or contradictory molecular profiles or for entities where methylation profiling provides useful information for patient risk stratification, for example in medulloblastoma and ependymoma. […] The ambiguity of traditional histopathological criteria to inform clinical oncologists on patient management, and the patients of the prognosis, called for a radically new approach for tumour diagnostics, leading to the development of a comprehensive CNS tumour reference cohort based on genome-wide DNA methylation profiles. […] We found a change of diagnosis in approximately 25% of patients, refinement in approximately 50% and confirmation of the diagnosis in 25%. In a proportion of cases where the diagnosis changed, there was a significant impact on treatment and clinical management, and in others, the provision of accurate integrated diagnosis prevented from unnecessary, potentially harmful treatment. […] Our cohort further highlights the essential role of the methylation array as a diagnostic tool in advanced brain tumour diagnostics, when integrated into the diagnostic pathway in a structured fashion as outlined in Fig. 8 and Fig. 9.

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