Establishment an early precise diagnosis model for bone metastasis in lung cancer based on driver gene types.

Abstract

Lung cancer has become the type of cancer with the highest incidence rate and the greatest threat of death worldwide. Bone is one of the common distant metastasis sites of lung cancer. Once a patient experiences bone metastasis, it will seriously affect the patient's quality of life and survival period. This study aimed to investigate the impact of driver gene mutations on the anatomical distribution patterns of BM in lung cancer and develop a prediction model for specific BM sites of lung cancer. From 2019 to 2023, we enrolled 353 lung cancer patients diagnosed with BM at our hospital. Associations between driver gene types and BM sites or timing were analyzed using chi-square. A predictive model was constructed through logistic regression, and its performance was evaluated using AUC, DCA, calibration curves, and independent cohort validation. The research found that driver gene types were significantly associated with the risk of simultaneous pelvic metastasis (P < 0.001). Logistic regression analysis identified five independent risk factors for simultaneous pelvic metastasis in lung cancer. The model demonstrated good predictive performance, with AUC values of 0.881 in the training set and 0.816 in the validation set. This study presents a novel analysis of the relationship between driver gene types and the spatio-temporal patterns of BM. Based on driver gene types, we developed an accurate nomogram model for predicting BM in lung cancer.