Clinical utility of pretreatment serum squamous cell carcinoma antigen for prognostication and decision-making in patients with early-stage cervical cancer

Background:To investigate the prognostic role of pretreatment squamous cell carcinoma antigen (SCCA) in early-stage cervical cancer (CC). Methods:We enrolled 487 cases of pathology-proven early-stage [International Federation of Gynecology and Obstetrics (FIGO) I/II] squamous or adenosquamous CC that were treated from 2012 to 2015. Restricted cubic splines (RCS) with a full Cox regression model were used to evaluate the association between SCCA levels and survival outcomes. Recursive partitioning analysis (RPA) was used to construct a risk stratification model for overall survival (OS). The performance of the RPA-based model was assessed using a receiver operating characteristic (ROC) curve. Results:RCS analysis revealed an association between SCCA and OS and disease-free survival (DFS); SCCA > 2.5 ng/mL was robust for risk discrimination in our cohort. SCCA had an interaction effect with FIGO classification: Patients with FIGO I and SCCA > 2.5 ng/mL overlapped with those with FIGO II and SCCA < 2.5 ng/mL for OS [hazard ratio, 1.04 (95% confidence interval (CI): 0.49-2.24), p = 0.903] and DFS [1.05 (0.56-1.98), p = 0.876]. RPA modeling incorporating SCCA (<2.5 ng/mL and > 2.5 ng/mL) and FIGO classification divided CC into three prognostic groups: RPA I, FIGO stage I, and SCCA < 2.5 ng/mL; RPA II, FIGO stage I, and SCCA > 2.5 ng/mL, or FIGO stage II and SCCA < 2.5 ng/mL; and RPA III, FIGO stage II, and SCCA > 2.5 ng/mL; with 5-year OS of 94.0%, 85.1%, and 73.5%, respectively (p < 0.001). ROC analysis confirmed that the RPA model outperformed the FIGO 2018 stage with significantly improved accuracy for survival prediction [area under the curve: RPA versus FIGO, 0.663 (95% CI: 0.619-0.705] versus 0.621 (0.576-0.664), p = 0.045]. Importantly, the RPA groupings were associated with the efficacy of treatment regimens. Surgery followed by adjuvant treatment had a higher OS (p < 0.01) and DFS (p = 0.024) than other treatments for RPA III, whereas outcomes were comparable among treatment regimens for RPA I-II. Conclusion:Herein, the role of SCCA for prognostication was confirmed, and a robust clinicomolecular risk stratification system that outperforms conventional FIGO classification in early-stage squamous and adenosquamous CC was presented. The model correlated with the efficacy of different treatment regimes.