Abstract
BackgroundDistinguishing hydatidiform moles (HMs) from non-molar specimens and the subclassification of HM are important because complete hydatidiform mole (CHM) is associated with an increased risk of gestational trophoblastic neoplasia. However, diagnosis based solely on morphology has poor interobserver reproducibility. Recent studies have demonstrated that the use of p57KIP2 immunostaining improves diagnostic accuracy for CHM.MethodsWe will conduct a systematic review of prospective and retrospective studies to evaluate the accuracy of p57KIP2 immunostaining compared with molecular genotyping for the diagnosis of CHM. A high-sensitivity search strategy will be employed in MEDLINE, EMBASE, LILACS, The Grey Literature Report, OpenGrey, OAIster, and Cochrane CENTRAL. Two reviewers will independently screen all identified references for eligibility and extract data. The methodological quality and bias of the included studies will be assessed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, and the overall quality of evidence will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. If a meta-analysis is possible, pooled estimates of sensitivity, specificity, and positive and negative likelihood ratios will be calculated using bivariate random-effects models. Statistical heterogeneity will be evaluated with I 2 statistics and explored through sensitivity analysis.DiscussionThere is considerable overlap between the histological features of molar and non-molar pregnancies and between complete and partial HMs, which results in significant interobserver variability in the diagnosis of CHM and its mimics. Therefore, molecular techniques are used to correctly diagnosis and treat CHM. However, these molecular diagnostic methods are technically difficult to perform, relatively costly, and unavailable in most pathology laboratories. According to our results, p57KIP2 immunostaining appears to be a practical and accurate adjunct for the diagnosis of CHM and its mimics because this technique is relatively simple, reliable, cost-efficient, and rapid. This systematic review will help to determine whether p57KIP2 immunostaining is an adequate alternative diagnostic test for CHM.Systematic review registrationPROSPERO CRD42015024181 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0349-7) contains supplementary material, which is available to authorized users.
Highlights
Distinguishing hydatidiform moles (HMs) from non-molar specimens and the subclassification of Hydatidiform mole (HM) are important because complete hydatidiform mole (CHM) is associated with an increased risk of gestational trophoblastic neoplasia
There is considerable overlap in histological features between molar and non-molar pregnancies and between CHMs and partial hydatidiform mole (PHM), which results in significant interobserver variability in the diagnosis of HM and its mimics
Correct diagnosis of these difficult cases may require molecular techniques that examine the differences in Deoxyribonucleic acid (DNA) content between CHM and PHM, including flow or image cytometric DNA analysis, chromosome in situ hybridization, polymerase chain reaction-based genotyping, or Human leukocyte antigen (HLA) typing
Summary
Design The methodological approach for evidence searching and synthesis described in this protocol will conform to the Cochrane Collaboration’s methods for assessing diagnostic test accuracy [5]. A sensitivity analysis will be conducted to assess the impact of including studies with 20 % or more missing data. Outcomes The primary outcome will be the diagnostic accuracy of p57KIP2 immunostaining for the diagnosis of CHM, which will be described based on sensitivity and specificity, negative and positive predictive values, and positive and negative likelihood ratios wherever possible. The sensitivity, specificity, positive predictive value, false-positive rate, and positive likelihood ratio will be calculated from the cut-off values of the index test. We will perform a sensitivity analysis to examine the effect of sample size and missing data on the results of the review. The presence of publication bias will be assessed by performing a regression of lnDOR and the effective sample size (ESS) based on methods described by Deeks et al [15]
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