# Average log change rate of pretreatment squamous cell carcinoma antigen after concurrent chemoradiotherapy in stage IIIC1 cervical squamous cell carcinoma

## TL;DR

This study shows that combining pretreatment SCC-Ag levels and their logarithmic change rate can better predict treatment outcomes in advanced cervical cancer patients.

## Contribution

The novel use of the average logarithmic change rate of SCC-Ag levels improves outcome prediction in cervical cancer treatment.

## Key findings

- Combining pretreatment SCC-Ag levels and their logarithmic change rate improves predictive accuracy over SCC-Ag alone.
- Higher SCC-Ag levels (≥ 5 ng/ml) and Cyfra levels (≥ 3.15 ng/ml) are significant predictors of disease-specific survival.
- Risk stratification using SCC-Ag levels and their logarithmic change rate distinguishes survival outcomes in cervical cancer patients.

## Abstract

We aimed to determine whether pretreatment squamous cell carcinoma antigen (SCC-Ag) levels and the average logarithmic change in SCC-Ag levels (\documentclass[12pt]{minimal}
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\begin{document}$$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$\end{document}ΔlogSCC-AgΔtime) after concurrent chemoradiotherapy (CCRT) could predict treatment outcomes in patients with stage IIIC1 cervical squamous cell carcinoma (SCC). We analyzed 168 patients with stage IIIC1 cervical SCC who underwent primary CCRT and collected data on age, local extension, treatment details, hematological parameters, and tumor markers such as SCC-Ag and carcinoembryonic antigen 21-1 (Cyfra). Predictive performances of pretreatment SCC-Ag levels and \documentclass[12pt]{minimal}
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\begin{document}$$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$\end{document}ΔlogSCC-AgΔtime were assessed using receiver operating characteristic curves. Survival analysis was performed using the Cox regression model and Kaplan–Meier plots. The combination of pretreatment SCC-Ag levels and \documentclass[12pt]{minimal}
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\begin{document}$$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$\end{document}ΔlogSCC-AgΔtime showed higher area under the curve values than pretreatment SCC-Ag levels alone (area under the curve; 95% confidence interval [CI] 0.708 [0.581–0.836] vs. 0.666 [0.528–0.804], respectively). Pretreatment SCC-Ag (≥ 5 ng/ml and Cyfra levels (≥ 3.15 ng/ml) and \documentclass[12pt]{minimal}
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\begin{document}$$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$\end{document}ΔlogSCC-AgΔtime (≥ − 1.575) were significant predictors of disease-specific survival. The 5-year disease-specific survival rates significantly differed among the low-, intermediate-, and high-risk groups. Risk stratification using both pretreatment SCC-Ag levels and \documentclass[12pt]{minimal}
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\begin{document}$$\frac{{\Delta \log \left( {{\text{SCC-Ag}}} \right)}}{{\Delta {\text{time}}}}$$\end{document}ΔlogSCC-AgΔtime may predict treatment outcomes of patients with stage IIIC1 SCC.