ObjectiveThis study aims to develop and validate a nomogram model that integrates autoantibodies and systemic inflammation markers to predict the risk of bone metastases in patients with non-small cell lung cancer (NSCLC). Additionally, we propose a novel approach for risk stratification and adjunctive assessment of bone metastases in NSCLC patients, aiming to support clinical decision-making.MethodsThis retrospective study analyzed 323 NSCLC patients treated at the Affiliated Hospital of Southwest Medical University from January 2020 to July 2024.
Comprehensive clinical, laboratory, and imaging data were collected. Key predictors included histology, TNM stage, ANA fluorescence patterns, anti-extractable nuclear antigens (anti-ENAs), SIRI, LWR, and anti-AMA-M2.
Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and variables with non-zero coefficients were incorporated into a nomogram. The model was validated internally using receiver operator characteristic curve (ROC) analysis, calibration curves, and decision curve analysis (DCA).
The incremental value of novel biomarkers was assessed using NRI and IDI.ResultsSeven variables were retained in the final nomogram, including histology, TNM stage, anti-ENAs, SIRI, LWR, anti-AMA-M2, and ANA fluorescence pattern.
Frontiers in Immunology published a clinical update in Infectious Disease on 26 May 2026.
The item focuses on Autoantibodies combined with systemic inflammation markers for predicting bone metastases in non-small cell lung cancer patients.
Review the original article for the full source wording and details.