Characterization of Patients with Non-Small Cell Lung Cancer Using Machine Learning Tools: Relationship with Prognostic Biomarkers and Overall Survival—A Pilot Study

Background/Objectives: Most cases of non-small cell lung cancer (NSCLC) are diagnosed at advanced stages, where prognosis remains poor. Machine learning (ML) offers new opportunities for patient stratification. This study aimed to identify distinct subgroups of patients with advanced-stage NSCLC using unsupervised ML techniques and to evaluate their association with survival outcomes and biomarker expression. Methods: 400 patients with advanced-stage NSCLC were analyzed using the k-prototypes algorithm. Clinical, demographic, and analytical variables, including smoking history and comorbidities, were incorporated. Identified clusters were compared in terms of molecular biomarkers and overall survival. A multivariable Cox proportional hazards model was performed to assess the association between cluster membership and overall survival. Results: Five patient profiles were identified. Cluster F, characterized by a predominance of women, relatively low smoking exposure, and a higher frequency of epidermal growth factor receptor (EGFR) mutations, showed the most favorable survival profile. Cluster S comprised mainly male heavy smokers with poorer performance status and high metastatic burden, whereas Cluster Y consisted predominantly of younger men without comorbidities but with frequent M1c disease. Clusters E and O showed intermediate outcomes and were characterized by older age with pleural effusion and by an older predominantly male smoking profile, respectively. In the multivariable Cox model, compared with Cluster F, a higher risk of death was observed for Cluster S (HR 1.62, 95% CI 1.01–2.60; p = 0.045) and Cluster Y (HR 1.55, 95% CI 1.09–2.21; p = 0.015), although the association for Cluster S should be interpreted cautiously. Differences in molecular biomarker distribution were also observed across clusters, particularly for EGFR mutations and programmed death-ligand 1 (PD-L1) expression. Conclusions: In this single-centre retrospective pilot study, unsupervised ML identified distinct patient profiles associated with differences in survival outcomes and molecular characteristics. These findings support the potential of data-driven approaches to characterize heterogeneity in advanced-stage NSCLC, although external validation is required before clinical application.