Development and validation of a risk prediction model for lymph node metastasis in stage IA2-IIA1 cervical cancer based on laboratory parameters.

Objective: To develop and validate a risk prediction model for lymph node metastasis (LNM) in stage IA2-IIA1 cervical cancer (CC) using laboratory parameters to aid in preoperative risk assessment and personalized treatment planning.

Methods: A retrospective analysis was conducted on 624 patients treated between 2017 and 2023, divided into a training group (418 patients) and a validation group (206 patients). Clinical and laboratory data, including squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), platelet count (PLT), fibrinogen (FIB), and C-reactive protein (CRP), were collected. Independent risk factors for LNM were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression. A predictive model was constructed and evaluated using receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and calibration curve.

Results: SCC-Ag, CEA, CA125, PLT, FIB, and CRP were identified as significant predictors of LNM, with SCC-Ag demonstrating an AUC of 0.811 (sensitivity: 65.00%, specificity: 93.08%). The model achieved an AUC of 0.969 in the training group and 0.942 in the validation group, indicating robust generalizability and high predictive accuracy. DCA confirmed the model's clinical utility across a wide range of risk thresholds, and the calibration curve showed a good agreement between predicted and observed outcomes.

Conclusions: This laboratory parameter-based risk prediction model is a reliable and practical tool for assessing LNM risk in stage IA2-IIA1 CC patients, supporting better clinical decision-making and reducing unnecessary interventions.