Integrated multiparametric ultrasound and biomarker assessment for clinical staging and treatment response prediction in cervical cancer: A single-center retrospective study.

Authors

Tao Lyu, Minghui Li, Huimin Li, Ling He, Wei Zhang, Xuan Shi

Published in

Cancer biomarkers : section A of Disease markers. Volume 43. Pages 18758592261479724. Epub Aug 22, 2026.

Abstract

BackgroundAccurate staging and treatment response assessment remain challenging in cervical cancer management. This study investigated the diagnostic and prognostic value of integrating ultrasonographic parameters with serum and tissue biomarkers.MethodsThis retrospective study included 392 patients with histopathologically confirmed cervical cancer treated between January 2019 and December 2024. All patients underwent comprehensive ultrasonographic evaluation and biomarker assessment (SCC-Ag, CEA, CA125, CA19-9, NLR, Ki-67, HPV status) before treatment. An integrated scoring system was developed and validated. In 324 patients receiving concurrent chemoradiotherapy, dynamic changes were monitored at baseline, mid-treatment, and post-treatment. Multivariate analysis identified independent prognostic factors for survival analysis.ResultsThe integrated scoring system demonstrated superior staging performance (sensitivity 92.6%, specificity 88.4%, AUC 0.94) compared to ultrasound alone (78.4%, 72.6%, 0.82) or biomarkers alone (74.2%, 68.4%, 0.78). Tumor volume strongly correlated with SCC-Ag levels in squamous cell carcinoma. The combination of >50% tumor volume reduction and >60% SCC-Ag decrease at mid-treatment predicted complete response with 91.2% sensitivity and 86.4% specificity. Prognostic stratification identified three risk groups with significantly different 3-year progression-free survival rates: low-risk 62.1%, intermediate-risk 54.6%, and high-risk 33.8%.ConclusionsIntegration of ultrasonographic parameters and biomarker profiles provides superior diagnostic accuracy for cervical cancer staging and enables early prediction of treatment response, potentially guiding personalized treatment decisions and surveillance strategies.

PMID:
42631493
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.

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