To establish a prognostic model of epidermal growth factor receptor mutated non-small cell lung cancer patients based on Least Absolute Shrinkage and Selection Operator regression.

Abstract

Background: There is currently a shortage of effective diagnostic tools that are used for identifying long-term survival among non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations. This research utilized the development of a prognostic model to assist clinicians in forecasting the survival over 24 months.

Methods: In Phase III and IV those patients who were diagnosed with EGFR mutation from January 2018 to June 2022 were enrolled into the lung cancer group of Thoracic Surgery Department of Hebei Provincial People's Hospital. Long-run survival was stated as survival for 24 months after being diagnosed. A multivariate prognostic pattern was constructed by means of internal validation and binary logistic regression by bootstrapping. One nomogram was created with a view to boosting the explanation and applicability of the pattern.

Results: A total of 603 patients with EGFR mutation were registered. Elements linked to the whole survival beyond 24 months were age (OR 6.15); female (OR 1.79); functional status (ECOG 0-1) (OR 5.26); Exon 20 insertion mutation deletion (OR 2.08); No central nervous system metastasis (OR 2.66), targeted therapy (OR 0.43); Immunotherapy (OR 0.24). The model has good internal validation.

Conclusion: Seven pretreatment clinicopathological variables predicted survival over 24 months. That pattern owns a great discriminative capability. It is hypothesized that this pattern is capable of assisting in selecting the optimal treatment sequence for NSCLC patients with EGFR mutations.