606
Views
8
CrossRef citations to date
0
Altmetric
Original Articles

Personalized charts for the fetal corpus callosum length

ORCID Icon, , , , , , & show all
Pages 3931-3938 | Received 11 Dec 2017, Accepted 17 May 2018, Published online: 07 Jun 2018
 

Abstract

Objective: To personally customize the antenatal ultrasound charts for the fetal corpus callosum (CC) length.

Methods: A retrospective analysis of fetal neuro-sonography scans. Cases were grouped as normal neuro-sonographic evaluation (normal) or as high risk and suspected brain anomaly (abnormal). The normal group was subcategorized according to Cignini’s CC length charts. Data of fetuses with a CC length between the 5th–95th percentile served for creating new charts, describing the ratio of the CC length to the major biometric parameters as a function of gestational age (GA).

Results: A total of 410 measurements were included. Of them 255 were normal and 155 abnormal. The CC length/estimated fetal weight (EFW) ratio had the strongest linear association with GA (R2 = 0.929). Applying charts using this ratio to the normal group, significantly increased the percent of CC length measurements defined as normal from 84.7 to 94.5% (p < .001). Conversely, applying these charts to the abnormal group nonsignificantly decreased the number of measurement defined as normal from 89 to 83.2% (p = .137)

Conclusions: The CC length/EFW ratio is strongly and linearly associated with GA. Using this personalized ratio may improve the diagnostic accuracy of CC evaluation by adjusting the CC length to the fetus natural proportions.

Disclosure statement

The authors report no conflicts of interest.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.