Correlations of atrial diameter and frontooccipital horn ratio with ventricle size in fetal ventriculomegaly

Volume development changes in the occipital lobe gyrus assessed by MRI in fetuses with isolated ventriculomegaly correlate with neurological development in infancy and early childhood

OBJECTIVE

This study was to systematically assess the occipital lobe gray and white matter volume of isolated ventriculomegaly (IVM) fetuses with MRI and to follow up the neurodevelopment of participants.

METHOD

MRI was used to evaluate 37 IVM fetuses and 37 control fetuses. The volume of gray and white matter in each fetal occipital gyrus was manually segmented and compared, and neurodevelopment was followed up and assessed in infancy and early childhood.

RESULT

Compared with the control group, the volume of gray matter in occipital lobe increased in the IVM group, and the incidence of neurodevelopmental delay increased.

CONCLUSION

We tested the hypothesis that prenatal diagnosis IVM represents a biological marker for development in fetal occipital lobe. Compared with the control group, the IVM group showed differences in occipital gray matter development and had a higher risk of neurodevelopmental delay.

Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly

OBJECTIVES

To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs.

METHODS

Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA.

CONCLUSIONS

Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs.

CLINICAL RELEVANCE STATEMENT

It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations.

KEY POINTS

Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Comparison of fetal and neonatal sonographic measurements of ventricular size in second- and third-trimester fetuses with or without ventriculomegaly: cross-sectional three-dimensional ultrasound study

OBJECTIVES

To assess, in a population comprising normal fetuses and fetuses with primary or post-hemorrhagic ventriculomegaly, the reproducibility of measurement of neonatal ultrasound indices in the fetus and to compare the performance of various cut-offs of these parameters to diagnose ventriculomegaly and classify its severity.

METHODS

This was a retrospective cross-sectional study including 182 singleton fetuses assessed by transvaginal neurosonography. The sample populations included 116 normal fetuses and 66 fetuses with primary (n = 56) or post-hemorrhagic (n = 10) ventriculomegaly. In all cases, the atrial width (AW) was measured according to standard protocols and the findings were compared with four sonographic indices developed in the neonate: the anterior horn width (AHW), the ventricular index (VI), the thalamo-occipital distance (TOD) and the fronto-occipital horn ratio (FOHR). Reproducibility of measurements was assessed using the intraclass correlation coefficient (ICC) and diagnostic accuracy of the neonatal indices was assessed against AW using areas under the receiver-operating-characteristics curves (AUC).

RESULTS

The intra- and interoperator reproducibility of measurement of AW and the neonatal measurements was excellent, with ICCs > 0.99 for all measures. The association in the fetus of all four variables developed in the neonate with the degree of ventriculomegaly as defined by the AW was strong for severe ventriculomegaly (AW > 15.0 mm; all AUC > 0.95), whereas the separation of cases with mild ventriculomegaly (AW, 10.0-15.0 mm) from those with normal AW (< 10.0 mm) was less effective.

CONCLUSIONS

When applied in the fetus, all four indices of ventriculomegaly developed in neonates (AHW, VI, TOD, FOHR) were associated strongly with fetal AW when the AW measurement indicated severe fetal ventriculomegaly. However, for mild ventriculomegaly, the association was weaker, probably due to the fact that, in the fetus, mild ventriculomegaly is not caused by obstruction of the ventricular system. Considering the similar performance of the four neonatal variables and the technical issues involved in determination of TOD and FOHR in the fetus, use of VI and AHW is preferred. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Multiple Machine Learning Approaches for Morphometric Parameters in Prediction of Hydrocephalus

Background: The diagnosis of hydrocephalus is mainly based on imaging findings. However, the significance of many imaging indicators may change, especially in some degenerative diseases, and even lead to misdiagnosis. Methods: This study explored the effectiveness of commonly used morphological parameters and typical radiographic findings in hydrocephalus diagnosis. The patients’ imaging data were divided into three groups, including the hydrocephalus group, the symptomatic group, and the normal control group. The diagnostic validity and weight of various parameters were compared between groups by multiple machine learning methods. Results: Our results demonstrated that Evans’ ratio is the most valuable diagnostic indicator compared to the hydrocephalus group and the normal control group. But frontal horns’ ratio is more useful in diagnosing patients with symptoms. Meanwhile, the sign of disproportionately enlarged subarachnoid space and third ventricle enlargement could be effective diagnostic indicators in all situations. Conclusion: Both morphometric parameters and radiological features were essential in diagnosing hydrocephalus, but the weights are different in different situations. The machine learning approaches can be applied to optimize the diagnosis of other diseases and consistently update the clinical diagnostic criteria.

Acquired hydrocephalus is associated with neuroinflammation, progenitor loss, and cellular changes in the subventricular zone and periventricular white matter

BACKGROUND

Hydrocephalus is a neurological disease with an incidence of 80-125 per 100,000 births in the United States. Neuropathology comprises ventriculomegaly, periventricular white matter (PVWM) alterations, inflammation, and gliosis. We hypothesized that hydrocephalus in a pig model is associated with subventricular and PVWM cellular alterations and neuroinflammation that could mimic the neuropathology described in hydrocephalic infants.

METHODS

Hydrocephalus was induced by intracisternal kaolin injections in 35-day old female pigs (n = 7 for tissue analysis, n = 10 for CSF analysis). Age-matched sham controls received saline injections (n = 6). After 19-40 days, MRI scanning was performed to measure the ventricular volume. Stem cell proliferation was studied in the Subventricular Zone (SVZ), and cell death and oligodendrocytes were examined in the PVWM. The neuroinflammatory reaction was studied by quantifying astrocytes and microglial cells in the PVWM, and inflammatory cytokines in the CSF.

RESULTS

The expansion of the ventricles was especially pronounced in the body of the lateral ventricle, where ependymal disruption occurred. PVWM showed a 44% increase in cell death and a 67% reduction of oligodendrocytes. In the SVZ, the number of proliferative cells and oligodendrocyte decreased by 75% and 57% respectively. The decrease of the SVZ area correlated significantly with ventricular volume increase. Neuroinflammation occurred in the hydrocephalic pigs with a significant increase of astrocytes and microglia in the PVWM, and high levels of inflammatory interleukins IL-6 and IL-8 in the CSF.

CONCLUSION

The induction of acquired hydrocephalus produced alterations in the PVWM, reduced cell proliferation in the SVZ, and neuroinflammation.

Not all ventriculomegaly is created equal: diagnostic overview of fetal, neonatal and pediatric ventriculomegaly

Fetal ventriculomegaly refers to a condition in which there is enlargement of the ventricular spaces, typically on prenatal ultrasound. It can be associated with other CNS or extra-CNS abnormalities, and this relationship is crucial to understand as it affects overall neonatal outcome. Isolated ventriculomegaly has been described in the literature with variable clinical outcome. Typically, outcome is based on the etiology and degree of ventriculomegaly. When associated with a pathologic condition, ventriculomegaly can be a result of hydrocephalus. While initial diagnosis is usually made on prenatal ultrasound, fetal magnetic resonance imaging is preferred to further elucidate any associated CNS malformations. In this paper, the authors aim to provide a comprehensive review of the diagnosis, associated etiologies, prognosis, and treatment options related to fetal, neonatal, and pediatric ventriculomegaly and hydrocephalus. In addition, preliminary data is provided from our institutional cohort of patients with a prenatal diagnosis of ventriculomegaly followed through the perinatal period.

Suprasellar arachnoid cysts in adults: clinical presentations, radiological features, and treatment outcomes

A tendency for suprasellar arachnoid cysts (SACs) to occur in young children is known. Data of adult SACs were rare in previous reports. The aim of this study is to discuss their clinical presentations, radiological features, and treatment outcomes based on 23 adult patients who underwent endoscopic fenestration in our hospital between January 2003 and December 2018. Preoperative cyst volume ranged from 12.3 to 72.5 cm³ (mean 39.8 ± 19.8). Endocrine disorders occurred in 7 (30.4%) patients. Hydrocephalus was observed in 20 patients. In the patients with hydrocephalus, the mean preoperative Evans' index (EI) (%) and frontooccipital horn ratio (FOHR) (%) were 44.8 (ranged 32.2-63.4) and 49.6 (ranged 36.7-59.8), respectively. A bivariate correlation showed significant positive association between preoperative cyst volume and preoperative EI or FOHR (Pearson correlation, r = 0.607, p = 0.005; r = 0.583, p = 0.007). The slit-valve phenomenon was observed in 13 (56.5%) patients. Pale/tenacious cyst walls were observed in 12 (52.2%) patients. Postoperatively, all patients achieved the improvement in clinical symptoms and a decrease in cyst size. The mean decreases in cyst volume, EI, and FOHR were 64.7%, 7.89%, and 5.8%, respectively. A bivariate correlation indicated the irrelevance between the postoperative cyst volume and postoperative EI or FOHR (Pearson correlation: r = 0.37, p = 0.11; r = 0.43, p = 0.054). These results reveal that there are a few differences in several aspects between adult patients and child patients. The severity of hydrocephalus is correlated with cyst size in adult patients. Additionally, the excellent outcomes in adult SACs can be obtained by endoscopic fenestration.

Volume growth trend and correlation of atrial diameter with lateral ventricular volume in normal fetus and fetus with ventriculomegaly: A STROBE compliant article

To explore the growth trend of fetal lateral ventricular volume, for understanding the relationship between atrial diameter (AD) and volume in normal fetus and fetus with ventriculomegaly.Overall, 97 sequential fetal head magnetic resonance imaging scans were performed; these pertained to 50 fetuses with normal lateral ventricles [normal group; gestational age (GA): 24-38 weeks] and 47 fetuses with ventriculomegaly (VM) (VM group; GA: 24-37 weeks). The left, right, and total lateral ventricular volume were measured using 3-dimensional magnetic resonance hydrography (MRH). Correlation coefficient (r) was calculated to assess the relationships of measurements. Lineal regression analysis was used to assess correlation of AD and GA with volume. Between-group differences in terms of AD and volume were assessed using t test.Significant linear growth was observed in the total lateral ventricular volume compared with GA in the normal group with a relative growth rate of 2.87% per week (P <.001). Significant linear relationship between AD and volume was observed, and a significant equation was acquired in the normal group and VM groups, respectively, using the simple linear regression model: left volume = 0.438 * normal left diameter (NLD) + 1.359; right volume = 0.493 * normal right diameter (NRD) + 1.012; left volume = 0.959 * left diameter in VM (VLD) - 2.074; right volume = 0.799 * right diameter in VM (VRD) - 0.443. A significant equation was obtained in the normal group and the VM group, using the multiple linear regression model: Total volume (mL) = 0.396 * NLD + 0.410 * NRD + 3.101; and total volume = 0.989 * VLD + 0.834 * VRD - 3.141, respectively. In terms of AD and volume, the left lateral ventricle was significantly larger than the right side in both groups. The volume of lateral ventricle in AD ≥10 mm group was larger than that in the AD <10 mm group. The total volume in the VM group was significantly larger than that in the normal group.The total lateral ventricular volume increased with GA. AD can be used to evaluate the fetal ventricular volume.

Benign Postnatal Outcome after Prenatal Diagnosis of Fetal Ventriculomegaly with Choroid Plexus Hyperplasia: A Case Report

Prenatal counselling following the diagnosis of fetal ventriculomegaly is challenging. Fetal MRI (magnetic resonance imaging) can be helpful in characterizing ventriculomegaly severity and associated anomalies, hence contributing to prognosis establishment. Choroid plexus hyperplasia (CPH) is a rare entity characterized by enlargement of the choroid plexuses, usually progressing to severe hydrocephalus with an associated poor outcome. We present a case of CPH diagnosed by fetal MRI at 23 weeks of gestation following referral for ventriculomegaly. The pregnancy was carried to term and the child was monitored clinically and radiologically. Despite the persistence of enlarged choroid plexuses, the ventricular size has progressively decreased, and at the 4-year follow-up the child presented normal psychomotor development. This case highlights the added value of MRI in prenatal diagnosis of fetal ventriculomegaly and its management. The unusual benign outcome in this case can be considered for parental counselling when faced with a fetus with similar findings.

Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly

IMPORTANCE

Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear.

OBJECTIVE

To determine whether extraction of multiple imaging features from fetal magnetic resonance imaging (MRI) and integration using machine learning techniques can predict which patients require postnatal cerebrospinal fluid (CSF) diversion after birth.

DESIGN, SETTING, AND PATIENTS

This retrospective case-control study used an institutional database of 253 patients with fetal ventriculomegaly from January 1, 2008, through December 31, 2014, to generate a predictive model. Data were analyzed from January 1, 2008, through December 31, 2015. All 25 patients who required postnatal CSF diversion were selected and matched by gestational age with 25 patients with fetal ventriculomegaly who did not require CSF diversion (discovery cohort). The model was applied to a sample of 24 consecutive patients with fetal ventriculomegaly who underwent evaluation at a separate institution (replication cohort) from January 1, 1998, through December 31, 2007. Data were analyzed from January 1, 1998, through December 31, 2009.

EXPOSURES

To generate the model, linear measurements, area, volume, and morphologic features were extracted from the fetal MRI, and a machine learning algorithm analyzed multiple features simultaneously to find the combination that was most predictive of the need for postnatal CSF diversion.

MAIN OUTCOMES AND MEASURES

Accuracy, sensitivity, and specificity of the model in correctly classifying patients requiring postnatal CSF diversion.

RESULTS

A total of 74 patients (41 girls [55%] and 33 boys [45%]; mean [SD] gestational age, 27.0 [5.6] months) were included from both cohorts. In the discovery cohort, median time to CSF diversion was 6 days (interquartile range [IQR], 2-51 days), and patients with fetal ventriculomegaly who did not develop symptoms were followed up for a median of 29 months (IQR, 9-46 months). The model correctly classified patients who required CSF diversion with 82% accuracy, 80% sensitivity, and 84% specificity. In the replication cohort, the model achieved 91% accuracy, 75% sensitivity, and 95% specificity.

CONCLUSION AND RELEVANCE

Image analysis and machine learning can be applied to fetal MRI findings to predict the need for postnatal CSF diversion. The model provides prognostic information that may guide clinical management and select candidates for potential fetal surgical intervention.