Methodological quality of fetal brain structure charts for screening examination and targeted neurosonography: a systematic review

Introduction: The methodological quality of fetal brain charts has not been critically appraised yet. Material and methods: MEDLINE, EMBASE, CINAHL and the Web of Science databases were searched electronically up to December 31, 2020. The primary outcome was to evaluate the methodology of the studies assessing the growth of fetal brain structures throughout gestation. A list of 28 methodological quality criteria divided into three domains according to "study design", "statistical and reporting methods", and "specific relevant neurosonography aspects" was developed in order to assess the methodological appropriateness of the included studies. The overall quality score was defined as the sum of low risk of bias marks, with the range of possible scores being 0-28. This quality assessment was applied to each individual study reporting reference ranges for fetal brain structures. Results: Sixty studies were included in the systematic review. The overall mean quality score of the studies included in this review was 51.3%. When focusing on each of the assessed domains, the mean quality score was 53.7% for "study design", 54.2% for "statistical and reporting methods" and 38.6% for "specific relevant neurosonography aspects". The sample size calculation, the correlation with a postnatal imaging evaluation and the whole fetal brain assessment were the items at the highest risk of bias for each domain assessed, respectively. The subgroup analysis according to different anatomical location showed the lowest quality score for ventricular and periventricular structures and the highest for cortical structures. Conclusions: Most previously published studies reporting fetal brain charts suffers from poor methodology and are at high risk of biases, mostly when focusing on neurosonography issues. Further prospective longitudinal studies aiming at constructing specific growth charts for fetal brain structures should follow rigorous methodology to minimize the risk of biases, guarantee higher levels of reproducibility and improve the standard of care.