ArticleCancer biomarkers : section A of Disease markers
Integrated multiparametric ultrasound and biomarker assessment for clinical staging and treatment response prediction in cervical cancer: A single-center retrospective study.
Article in Cancer biomarkers : section A of Disease markers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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