The association between incidence and mortality of brain cancer and human development index (HDI): an ecological study

Khazaei, Zaher; Goodarzi, Elham; Borhaninejad, Vahidreza; Iranmanesh, Farhad; Mirshekarpour, Hosein; Mirzaei, Batool; Naemi, Hasan; Bechashk, Sayeed Maryam; Darvishi, Isan; Sarabi, Roghayeh Ershad; Naghibzadeh‐Tahami, Ahmad

doi:10.1186/s12889-020-09838-4

Cited by 56 publications

(38 citation statements)

References 33 publications

(33 reference statements)

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“…In terms of the geographical distribution of the prediction models, all models were developed in countries with high human development index where early detection rate is likely higher than in developing countries ( Khazaei et al, 2020 ). Prediction models tailored to the population in less developed countries are needed before generalization and applications in clinical use.…”

Section: Discussionmentioning

confidence: 99%

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review

et al. 2022

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PurposeAlthough an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.MethodsMEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy,” “brain,” “prediction model,” and “neurocognitive impairments.” Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.ResultsOf 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n = 7) and the trail making tests (n = 4) were the most frequent outcome assessment tools. All studies used regression (n = 14 linear, n = 8 logistic, and n = 4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.ConclusionExisting models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

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Section: Discussionmentioning

confidence: 99%

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review

et al. 2022

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“…In terms of the geographical distribution of the prediction models, all models were developed in countries with high human development index where early detection rate is likely higher than in developing countries [50]. Prediction models tailored to the population in less developed countries are needed before generalization and applications in clinical use.…”

Section: Discussionmentioning

confidence: 99%

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

Tohidinezhad

Perri

Zegers

et al. 2021

Preprint

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Purpose: Although an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.Methods: MEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy”, “brain”, “prediction model”, and “neurocognitive impairments”. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.Results: Of 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning (n=7) and the trail making tests (n=4) were the most frequent outcome assessment tools. All studies used regression (n=14 linear, n=8 logistic, and n=4 Cox) as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.Conclusion: Existing models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: develop a standardized instrument for neurocognitive assessment in patients with brain tumors; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments.

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“…Tumors of the brain are rare but deadly entities whose treatment remains challenging. 1 Glioblastoma (GBM; World Health Organization [WHO] grade IV glioma) is the most frequent and aggressive primary brain tumor in adults. Life expectancy remains bleak at around 15 to 19 months.…”

Section: Introductionmentioning

confidence: 99%

Repurposing of Anticancer Stem Cell Drugs in Brain Tumors

Bahmad

Daher

Aljamal

et al. 2021

J Histochem Cytochem.

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Brain tumors in adults may be infrequent when compared with other cancer etiologies, but they remain one of the deadliest with bleak survival rates. Current treatment modalities encompass surgical resection, chemotherapy, and radiotherapy. However, increasing resistance rates are being witnessed, and this has been attributed, in part, to cancer stem cells (CSCs). CSCs are a subpopulation of cancer cells that reside within the tumor bulk and have the capacity for self-renewal and can differentiate and proliferate into multiple cell lineages. Studying those CSCs enables an increasing understanding of carcinogenesis, and targeting CSCs may overcome existing treatment resistance. One approach to weaponize new drugs is to target these CSCs through drug repurposing which entails using drugs, which are Food and Drug Administration–approved and safe for one defined disease, for a new indication. This approach serves to save both time and money that would otherwise be spent in designing a totally new therapy. In this review, we will illustrate drug repurposing strategies that have been used in brain tumors and then further elaborate on how these approaches, specifically those that target the resident CSCs, can help take the field of drug repurposing to a new level.

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The association between incidence and mortality of brain cancer and human development index (HDI): an ecological study

Cited by 56 publications

References 33 publications

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients With Primary or Secondary Brain Tumors: A Systematic Review

Prediction Models for Radiation-Induced Neurocognitive Decline in Adult Patients with Primary or Secondary Brain Tumors: A Systematic Review

Repurposing of Anticancer Stem Cell Drugs in Brain Tumors

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