Survival Prediction in Patients With Epidermal Growth Factor Receptor-Mutated Non-small Cell Lung Cancer With Bone Metastases Receiving Icotinib Therapy: A Retrospective Observational Study.

IntroductionClinical prediction models for response to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with advanced non-small cell lung cancer (NSCLC) with bone metastasis (BoM) and EGFR mutations are lacking. This study evaluated predictors of response to the EGFR-TKI icotinib in patients with NSCLC and BoM.MethodsWe retrospectively analysed patients with EGFR-mutated NSCLC and BoM treated with icotinib at Wuhan Union Hospital. Least absolute shrinkage and selection operator and multivariate Cox regression analyses identified independent predictors of progression-free survival (PFS). A prognostic nomogram was developed, and its effectiveness was assessed using receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), and calibration curves.ResultsAmong 194 patients (106 with and 88 without BoM), median PFS in the BoM group was 9.8 months, with a 35.8% overall response rate, and 66.0% disease control rate. In univariate and multivariate Cox regression analyses, BoM was an independent predictor of PFS in the overall cohort (hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.34-3.07; P < 0.001). In the BoM subgroup, albumin (HR: 0.36; 95% CI: 0.21-0.62; P < 0.001), lactate dehydrogenase (LDH) (HR: 1.99; 95% CI: 1.21-3.28; P = 0.007), and the platelet-lymphocyte ratio (PLR) (HR: 1.85; 95% CI: 1.05-3.25; P = 0.033) were independently associated with PFS. A nomogram predicting 3-, 6-, and 12-month PFS achieved time-dependent areas under the ROC curve of 0.765, 0.743, and 0.724, respectively. Calibration plots and DCA demonstrated acceptable performance and potential clinical relevance.ConclusionsAlbumin, LDH, and PLR predict PFS in EGFR-mutated NSCLC with BoM treated with icotinib. The proposed nomogram may assist in preliminary risk stratification in this specific population.