Evidence Hub

Identifying low-volume metastases through ultrastaging of negative pelvic nodes in recurrent early-stage cervical cancer: a case series and literature review

T. Meschini, A. Giudici, Mariangela Longo, V. Artuso, G. Schivardi, F. Multinu, Sara Orlando, G. Bogani et al.

European Journal of Obstetrics, Gynecology, and Reproductive Biology, 2025

161 participants · 1 citation

Abstract

INTRODUCTION

Ultrastaging has been shown to improve the detection of macrometastases (MACs), micrometastases (MICs) and isolated tumour cells (ITCs). This study evaluated the role of ultrastaging in the management of cervical cancer through a single-centre retrospective analysis, along with a systematic literature review.

METHODS

This retrospective study included patients surgically treated for FIGO 2009 stage IA1-IB1 cervical cancer between January 2011 and December 2023 with negative lymph nodes and no adjuvant therapy based on tumour risk factors. Cases with isolated lymph node recurrence during follow-up were examined. Ultrastaging of negative nodes was performed to detect low-volume metastases. Additionally, a systematic review was conducted following PRISMA guidelines, including a PubMed search of all indexed records up to 1 June 2024. Eligible studies included those with more than three patients who underwent either sentinel lymph node (SLN) biopsy or pelvic lymphadenectomy, and included both standard and ultrastaging analyses of negative SLNs.

RESULTS

From a cohort of 161 patients, three (1.8 %) were included. At primary surgery, a median of 28 lymph nodes (range 12-55) were removed, all of which were negative on standard evaluation. Ultrastaging re-evaluation identified MICs in two cases. The systematic review included 13 studies. Ultrastaging detected metastases in 353 (18.1 %) patients: 147 (41.6 %) MACs, 132 (37.4 %) MICs, and 74 (21.0 %) ITCs, resulting in a…

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free