Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy

Sungura, R; Mpolya, Emmanuel; Jm, Spitsbergen; Onyambu, CK; Sauli, Elingarami; Vianney, J-M

doi:10.1016/j.ejro.2020.100281

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…Data sources/measurement of variables: the study involved the collection of brain CT-scan images, which were already scanned using 6, 16 and 128 slices using Siemens Somatom Emotion, Sensation and Definition machines respectively, with a slice thickness of 5mm and an increment of 2mm. From Figure 1 , a computer-assisted system was used to measure the brain volume of all the images using a DBF formula designed to measure the dimensions of the brain relative to the skull and ventricles [ 13 ]. This study considered cut-off for a DBF value of 0.75 to describe the presence of brain atrophy.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, a value <0.75 indicated the presence of atrophy, and a value >0.75 indicated normal brain volume. Additionally, to grade the severity of brain atrophy, this study considered the range and grade of brain atrophy as follows: 0.15 - 0.30 (very severe), 0.31 - 0.45 (severe), 0.46 - 0.60 (moderate), 0.61 - 0.75 (mild), and >0.75 (normal brain volume) [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

“…Age was collected from the hospital charts while DBF was a derived variable calculated according to Sungura, et al . 2020 [ 13 ]. As already described in the data source section, this study considered the cut-off for a DBF value of 0.75 to describe the presence or absence of brain atrophy.…”

Section: Methodsmentioning

confidence: 99%

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Prevalence and risk factors of brain atrophy and associated confusion state among adults from three hospitals in northern Tanzania

Joseph¹,

Mpolya²,

Vianney³

2023

Pan Afr Med J

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Introduction brain atrophy is the reduction of brain volume often accompanied with cognitive changes. Despite the availability of computerized-tomography (CT) scanners in Tanzania, little is known about the magnitude of brain atrophy, its associated confusion state and the risk factors in adults. This study aimed to fill those knowledge gaps. Methods a retrospective cross-sectional hospital-based survey was conducted in northern Tanzania using a sample size of 384 CT images of adults who underwent brain CT scans in three referral hospitals. CT images were evaluated using a diagonal brain fraction (DBF) method to determine the presence of brain atrophy. Data for other covariates were also collected. Results we report a prevalence of 60.67% for brain atrophy and 35% for the associated confusion state. Association between confusion state and brain atrophy was statistically significant (χ 2 = 21.954, p<0.001). Brain atrophy was prognosticated by: age (adjusted OR: 1.11; 95% CI [1.05, 1.20], p<0.001), smoking (adjusted OR: 6.97; 95% CI [2.12, 26.19], p<0.001), alcohol-consumption (adjusted OR: 11.87; 95% CI [3.44, 40.81], p<0.001), hypertension (adjusted OR: 61.21; 95 CI [15.20, 349.43], p<0.001), type-2 diabetes mellitus (adjusted OR: 15.67; 95% CI [5.32, 52.77], p<0.001) and white matter demyelination (adjusted OR: 13.45; 95% CI [4.66, 44.25], p<0.001). Conclusion there is high prevalence of brain atrophy and associated confusion state among hospitalized adults in northern Tanzania. Reported prognostic factors for brain atrophy such as age, smoking, alcohol consumption, hypertension, type-2 diabetes mellitus and white matter demyelination could help focus interventions in this area.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prevalence and risk factors of brain atrophy and associated confusion state among adults from three hospitals in northern Tanzania

Joseph¹,

Mpolya²,

Vianney³

2023

Pan Afr Med J

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show abstract

“…A radiologist examined the Brain CT-scan images, which evaluated sulcal width, lateral ventricular body width and the Evans index using the three familiar radiological linear procedures. Diagonal multi-linear (DBF) approach was calculated to identify the presence or absence of brain atrophy for those who qualified for the use of this technique [22].…”

Section: Methodsmentioning

confidence: 99%

A case-control study on the driving factors of childhood brain volume loss: What pediatricians must explore

et al. 2022

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Background The brain volume loss also known as brain atrophy is increasingly observed among children in the course of performing neuroimaging using CT scan and MRI brains. While severe forms of brain volume loss are frequently associated with neurocognitive changes due to effects on thought processing speed, reasoning and memory of children that eventually alter their general personality, most clinicians embark themselves in managing the neurological manifestations of brain atrophy in childhood and less is known regarding the offending factors responsible for developing pre-senile brain atrophy. It was therefore the goal of this study to explore the factors that drive the occurrence of childhood brain volume under the guidance of brain CT scan quantitative evaluation. Methods This study was a case-control study involving 168 subjects with brain atrophy who were compared with 168 age and gender matched control subjects with normal brains on CT scan under the age of 18 years. All the children with brain CT scan were subjected to an intense review of their birth and medical history including laboratory investigation reports. Results Results showed significant and influential risk factors for brain atrophy in varying trends among children including age between 14-17(OR = 1.1), male gender (OR = 1.9), birth outside facility (OR = 0.99), immaturity (OR = 1.04), malnutrition (OR = 0.97), head trauma (OR = 1.02), maternal alcoholism (OR = 1.0), antiepileptic drugs & convulsive disorders (OR = 1.0), radiation injury (OR = 1.06), space occupying lesions and ICP (OR = 1.01) and birth injury/asphyxia (OR = 1.02). Conclusions Pathological reduction of brain volume in childhood exhibits a steady trend with the increase in pediatric age, with space occupying lesions & intracranial pressure being the most profound causes of brain atrophy.

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Fractal Dimension Analysis in Neurological Disorders: An Overview

Díaz Beltrán,

Madan,

Finke

et al. 2024

Advances in Neurobiology

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Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy

Cited by 6 publications

References 42 publications

Prevalence and risk factors of brain atrophy and associated confusion state among adults from three hospitals in northern Tanzania

Prevalence and risk factors of brain atrophy and associated confusion state among adults from three hospitals in northern Tanzania

A case-control study on the driving factors of childhood brain volume loss: What pediatricians must explore

Fractal Dimension Analysis in Neurological Disorders: An Overview

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