Role of Diffusion Tensor Imaging in Diagnosis and Estimation of Shunt Effect for Hydrocephalus in Stroke Patients: A Narrative Review

Cho, Min-Jye

doi:10.3390/diagnostics12061314

Cited by 1 publication

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our research aligns with existing evidence indicating that treatment modality selection does not serve as a predictive factor for chronic hydrocephalus development (42). The primary cause of neurological deficits in hydrocephalus arises from increased pressure on periventricular neural structures due to excessive CSF accumulation (24). Although relative ventricular size measurements have served as an objective radiological evaluation tool, they do not accurately gage hydrocephalus impact on periventricular neural structures (24).…”

Section: Discussionsupporting

confidence: 70%

“…The primary cause of neurological deficits in hydrocephalus arises from increased pressure on periventricular neural structures due to excessive CSF accumulation ( 24 ). Although relative ventricular size measurements have served as an objective radiological evaluation tool, they do not accurately gage hydrocephalus impact on periventricular neural structures ( 24 ). Radiological imaging reveals changes in the periventricular and deep white matter, which may be attributed to chronic ischemia or edema associated with hydrocephalus ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

“…The periventricular white matter displays progressive axonal degradation and demyelination ( 44 , 45 ), influencing vital structures involved in motor control, sensory processing, and memory function such as the corpus callosum, internal capsule, fornix, and periventricular projection axons ( 46 ). Hence, diffusion tensor imaging (DTI) has been recommended as a valuable diagnostic tool for stroke patients with hydrocephalus ( 47 ), particularly in assessing fractional anisotropy (FA) values within the periventricular white matter adjacent to the anterior horn as a potential diagnostic biomarker ( 24 , 48 ). It’s important to note that compared to other neural structures within the periventricular region, hydrocephalus appears to significantly compress the anterior corona radiata ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

“…Ventricular width refers to the narrowest distance between walls on both sides of the lateral ventricle body, measured axially; CT slices should be taken where the cella media first appears unobscured by other regions (thalamus) ( 23 ). Diffusion Tensor Imaging (DTI) has revealed significant increases in periventricular and deep cerebral white matter fraction anisotropy for hydrocephalus compared with healthy elderly individuals; CT scans may exhibit decreased density in deep white matter, known as leukoporosis ( 24 ). In this study, patients are identified as positive if they display reduced white matter density around anterior horns intervals 7 days after admission, wherein CT values decrease by ≥5 HU in diameter 8 mm.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study

Rao,

Yang,

Enxi

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

ObjectiveOur aim was to develop a nomogram that integrates clinical and radiological data obtained from computed tomography (CT) scans, enabling the prediction of chronic hydrocephalus in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodA total of 318 patients diagnosed with subarachnoid hemorrhage (SAH) and admitted to the Department of Neurosurgery at the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2022 were enrolled in our study. We collected clinical characteristics from the hospital’s medical record system. To identify risk factors associated with chronic hydrocephalus, we conducted both univariate and LASSO regression models on these clinical characteristics and radiological features, accompanied with penalty parameter adjustments conducted through tenfold cross-validation. All features were then incorporated into multivariate logistic regression analyses. Based on these findings, we developed a clinical-radiological nomogram. To evaluate its discrimination performance, we conducted Receiver Operating Characteristic (ROC) curve analysis and calculated the Area Under the Curve (AUC). Additionally, we employed calibration curves, and utilized Brier scores as an indicator of concordance. Additionally, Decision Curve Analysis (DCA) was performed to determine the clinical utility of our models by estimating net benefits at various threshold probabilities for both training and testing groups.ResultsThe study included 181 patients, with a determined chronic hydrocephalus prevalence of 17.7%. Univariate logistic regression analysis identified 11 potential risk factors, while LASSO regression identified 7 significant risk factors associated with chronic hydrocephalus. Multivariate logistic regression analysis revealed three independent predictors for chronic hydrocephalus following aSAH: Periventricular white matter changes, External lumbar drainage, and Modified Fisher Grade. A nomogram incorporating these factors accurately predicted the risk of chronic hydrocephalus in both the training and testing cohorts. The AUC values were calculated as 0.810 and 0.811 for each cohort respectively, indicating good discriminative ability of the nomogram model. Calibration curves along with Hosmer-Lemeshow tests demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. Furthermore, Brier scores (0.127 for the training and 0.09 for testing groups) further validated the predictive performance of our nomogram model. The DCA confirmed that this nomogram provides superior net benefit across various risk thresholds when predicting chronic hydrocephalus. The decision curve demonstrated that when an individual’s threshold probability ranged from 5 to 62%, this model is more effective in predicting the occurrence of chronic hydrocephalus after aSAH.ConclusionA clinical-radiological nomogram was developed to combine clinical characteristics and radiological features from CT scans, aiming to enhance the accuracy of predicting chronic hydrocephalus in patients with aSAH. This innovative nomogram shows promising potential in assisting clinicians to create personalized and optimal treatment plans by providing precise predictions of chronic hydrocephalus among aSAH patients.

show abstract

Section: Discussionsupporting

confidence: 70%