An Energy-Efficient Test and Predictive Model for Recurrence After Radiotherapy in Localized Intermediate and Advanced Cervical Cancer Were Created Using Thymidine Kinase 1 in Conjunction with Inflammatory Markers and Tumor Markers.

Purpose: Construction of a nomogram model based on Thymidine kinase 1 (TK1) in combination with inflammatory indicators and tumor markers to predict the probability of recurrence in mid- to late-stage cervical cancer. Methods: One hundred fourteen instances of intermediate and advanced cervical cancer admitted to our hospital's radiotherapy department between June 2017 and January 2023 were retrospectively studied. Logistic regression analysis includes variables relevant for univariate analysis. Meaningful indications from multifactor analysis were included in the nomogram model, the model's correctness was evaluated using the C-index, and the model's effectiveness was assessed using calibration curves, clinical decision curves, and clinical impact curves. Results: A nomogram model was created due to the logit regression analysis that revealed the squamous cell carcinoma antigen (SCC) and TK1 as independent recurrence predictors following cervical cancer radiation (P<0.05). The C index and Area Under the Curve (AUC) were 0.79 (95% CI 0.67-0.91). The AUC and C-index were both more extraordinary than those of TNM staging alone (C-index 0.57, 95% CI 0.43-0.71) and SCC alone (C-index 0.67, 95% CI 0.51-0.82). Calibration curves, Decision Curve Analysis (DCA), and clinical impact curves (CIC) indicate that the model predicts probabilities more accurately. Conclusion: The nomogram model based on TK1 combined with inflammatory markers and tumor markers is more reliable than the TNM staging and SCC systems alone for forecasting recurrence after radiotherapy in intermediate- and advanced-stage cervical cancer. It is also a cheap, practical, and simple-to-obtain model that can supplement the TNM staging system for forecasting prognosis and significantly enhances clinicians' decision-making.