Development and Validation of a Nomogram Integrating Body Composition and Inflammatory Markers to Predict Concurrent Chemoradiotherapy Response in Locally Advanced Cervical Cancer

Purpose: To develop and validate a nomogram integrating body composition and systemic inflammatory markers for predicting sensitivity to concurrent chemoradiotherapy (CCRT) in patients with locally advanced cervical cancer (LACC). Patients and Methods: This single-center retrospective study included 215 patients with FIGO 2018 stage IIB-IVA LACC who received first-line CCRT between September 2020 and September 2024. Patients were randomly assigned to a training set (n=150) and a test set (n=65). Treatment response was assessed approximately 8 weeks after radiotherapy according to RECIST version 1.1. Patients with complete or partial response were classified as CCRT-sensitive, whereas those with stable or progressive disease were classified as CCRT-resistant. Multivariable logistic regression was used to identify independent predictors and construct a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and was compared with clinical-only, systemic inflammatory, and body composition models. Results: Tumor size, platelet-to-lymphocyte ratio, prognostic nutritional index, skeletal muscle index, and visceral adipose index were identified as independent predictors of CCRT sensitivity and were incorporated into the nomogram. The model showed good discrimination, with an area under the ROC curve of 0.796 (95% CI, 0.714– 0.878) in the training set and 0.751 (95% CI, 0.618– 0.884) in the test set. At the optimal cutoff probability of 0.66, sensitivity and specificity were 75.7% and 72.1% in the training set, and 79.1% and 59.1% in the test set, respectively. Calibration curves showed good agreement, and DCA indicated favorable clinical utility. The nomogram outperformed the three single-domain models. Conclusion: We developed and internally validated a non-invasive nomogram integrating tumor size, systemic inflammatory markers, and CT-based body composition parameters to predict CCRT sensitivity in LACC. This model may assist in early risk stratification and individualized treatment planning, although external validation in larger multicenter cohorts is still needed.