Using Silva pattern system to predict prognosis and plan treatment of invasive endocervical adenocarcinoma: a single-center retrospective analysis

Author:

Li Xiao,Pang Shujie,Shen Yan,Qu Pengpeng

Abstract

AbstractBackgroundThis study evaluated the prognostic value of the Silva pattern system for invasive endocervical adenocarcinoma (EAC) by analysing its association with clinical and pathological features to provide more appropriate clinical management.MethodsA retrospective analysis including 63 patients with pathological diagnosis of invasive EAC was performed from March 2011 to December 2016 at our hospital. All pathological slides were reviewed by three senior pathologists, and cases were stratified into patterns A, B, or C by consensus according to the Silva pattern system criteria. Clinicopathological characteristics and follow-up of the three Silva subgroups were analysed.ResultsSilva A, B, and C EAC patients were compared based on tumour size, clinical stage, lymphovascular invasion (LVI), and depth of invasion (DOI). The differences were found to be statistically significant (p < 0.01). There was no statistically significant difference in the proportion of lymph node metastasis among the three groups (p > 0.05) or in the recurrence and mortality rates of patients with Silva A, B, and C EAC (p > 0.05). Single factor analysis showed that tumour size, clinical stage, lymph node metastasis, LVI, and DOI were related to postoperative recurrence, whereas age, Silva classification, and postoperative recurrence were not correlated.ConclusionThe Silva classification system can predict lymph node status and prognosis of invasive EAC, but it cannot be used as an independent indicator. Individualized treatment plans should be adopted for patients with EAC.

Funder

Tianjin Science and Technology Planning Project

Tianjin Health Research Project

Tianjin Science and Technology Committee Fund Project

Publisher

Springer Science and Business Media LLC

Subject

Obstetrics and Gynecology,Reproductive Medicine,General Medicine

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